Is Our Keith unexpectedly passionate about English liberty? I doubt it. This is a man who locks people up for mean tweets, and can’t bear to have Shakespeare, Elizabeth I or Walter Raleigh looking at him from the walls of No. 10. No: the most likely reason for Labour ruling out an overt ID policy is simply that it’s already coming. It’s just arriving in a piecemeal, cack-handed way, to the profit neither of the Government nor the electorate but rather private corporations, and under the cover of our established church: the NHS. 

I supported the campaign against Blair’s ID card bill in the 2000s. So it grieves me to admit this: but Blair was right then, and he’s right now. The UK’s administrative, political and economic order has long since left behind the kind of simplicity that can be managed without government ID. The historian AJP Taylor described that England, which in his words existed before August 1915: a country in which “a sensible, law-abiding Englishman could pass through life and hardly notice the existence of the state, beyond the post office and the policeman”.

That’s not the world we live in any more. We don’t even live in the relatively high-trust one of the mid-twentieth century, all paper forms and cheques in the post. Unless we adapt to the Britain we have now, our capacity to function at all will go on disintegrating. And given the endemic inability of our leadership class to build meaningful state capacity, across either party, adapting is in practice going to mean selling us all to Big Tech.

Our government evidently already accepts this. On Monday, Health Secretary Wes Streeting made much of a deal agreed by the Tories to unify the country’s currently scattered and poorly-integrated NHS patient records. The contractor, to the consternation of privacy and Palestine advocates alike, is Peter Thiel’s Palantir, purveyor of military and surveillance tech to (among others) Israel. 

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It should come as no surprise that Thiel, a keen biotech investor, is salivating at the prospect of access to such granular population-scale medical data. And for the government, too, the deal surely makes sense. As the BMJ reported, a million prescribing errors a year could be corrected by implementing a single digital record across the country, potentially saving lives in the process — not to mention the countless administrative hours saved when records no longer need to be exchanged manually between providers. But if the conspiracists aren’t already going bananas about this, they should be. Never mind objections on privacy grounds; this will almost certainly end up becoming digital ID by the back door.

The tinfoil hatters were already warning of this prospect during Covid, when eye-bleeding Facebook boomer memes predicted imminent global digital ID and sinister moves to establish One World Government and digital tyranny all powered by the biosecurity state. And well they might: while such voices were probably over-imaginative about the details, they were also directionally correct. In the UK, with its religiously nationalised health system, the most robust vector for digital ID really is the NHS. 

Covid-era international travel restrictions and vaccine mandates already took us halfway there. It was impossible to travel internationally, for a while, without both biometric ID and individual health data: national passports and so-called “vaccine passports” respectively. And if Thiel’s eye of Sauron is able to make all our NHS records interoperable, he can surely plug these into National Security numbers and passport data too. So why would Starmer even need to fight for such an unpopular measure as digital ID directly, when the needs of our sainted NHS make it all but an inevitability? 

But the reality is that it doesn’t matter what the memes say: digital tyranny is coming. We first saw it in Canada, when Justin Trudeau led the way in weaponising the banking system to end a protest against his vaccine mandates. Now, Starmer’s government is about to hand itself powers to demand information from any private company, or take money from your bank account or payslip, if it thinks you’re committing benefit fraud. Digging our heels in about ID achieves little save perpetuating an information asymmetry that favours corporations and criminals over the Government, forcing them to find other, more clumsy and intrusive means of achieving the same ends.

As for the privacy ship, that is also already a dot on the horizon. Every time we buy something online, use social media, accept a browser cookie, or download an app, we’re handing over personal information. Apple, Google, and Meta almost certainly have a more complete picture of you than the UK government. Which is more trustworthy? Difficult to say. But once the information is out there, it’s sold on to data brokers who will share all your internet habits with whoever has the money to pay for it. Only the Government is flying blind. This does not make sense.  

If the prospect seems alarming and un-British to you, well: it does to me as well. But rejecting ID cards would only make sense if we could somehow return to Taylor’s pre-1914 world, in which most people aren’t very mobile and in any case being a citizen doesn’t entitle you to much. And that’s not the Britain of today, in which information, money, and people are all hypermobile, to the extent that it’s widely considered rude to make assumptions about someone’s nationality based on their appearance, culture, or first language. 

There is a range of views on the merits of this modern approach. But one thing is certain: combining generous welfare with only the most rudimentary efforts to track who is actually entitled to it also means rich potential for bad actors to exploit gaps between databases. Earlier this year, for example, three Bulgarian nationals were sentenced for stealing hundreds of millions of pounds from the UK taxpayer, funding the renovation of an entire town in their home country. They sent “customers” on short visits to the UK using cheap flights, supplied forged proof of address, job offers and other evidence of entitlement to benefits, then trousered the payments that flowed in thereafter. 

As I’m sure Blair would be swift to point out, this would have been far easier to stop with greater interoperability between all the existing government databases, such as tax, driving licence, healthcare, and travel behaviour. The same also goes for the health tourism that costs the already-groaning UK taxpayer £500m a year. In a word: the Government probably already collates enough information to identify and stop scammers; it’s just held in a host of disparate databases. As things stand, we don’t even bother to check the passports of those exiting the UK, meaning we actually have no idea how many people there are in Britain today. If only there were some kind of sinister, CIA-funded surveillance tech Eye of Sauron out there, who could put the pieces together for us. 

Should we obtain such an Eye, one upside might be greater power to tackle the small boats crisis. For here, as the French are fond of pointing out, our lack of centralised ID is a significant pull factor for Channel migrants. This lacuna creates what Tories like to call a “dynamic labour market” and which everyone else calls sweatshops, money laundering, and an “undocumented” (and sometimes trafficked) underclass working cash-in-hand for poverty wages. And this in turn drives over-regulation and over-taxation of those foolish enough to hang on in what’s left of Britain’s formal economy, instead of — as growing numbers are now doing — fleeing the UK like rats leaving a sinking ship. 

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So if Dark Lord Tony is cheering on Palantir’s project to create a unified patient database, and advocating the sale of NHS data to fuel a biotech boom, perhaps he’s hoping that this time our hapless Westminster wonks will seize the opportunity to take a stake in every such venture. Perhaps, downstream of this, we might even actually start making stuff again — even if it’s mainly IP — rather than just juicing house prices and wiping bottoms. A girl can dream. But chances are he’s also identified NHS data as the most politically palatable way of sweetening the digital ID pill: after all, our national religion of NHS worship serves as an effective trump card in so many other debates, there’s a robust likelihood it will neutralise the WEF conspiracists too. 

And, as un-British as it is, and much as I dislike turning 180 on so creepy a prospect as government ID, the bottom line is that we can’t have it both ways. We can’t have radically dematerialised citizenship, highly mobile populations, global social media communications, and a taboo on linking citizenship to ethnicity, culture, or language, then also insist on a generous welfare state without robust central monitoring of who is entitled to what. The pre-1914 English relationship to citizenship and the state persists, as a ghostly collective memory, and junking it goes deeply against the grain – including for me. But those who cling to our tax-funded entitlements cannot then reject the logic of state managerialism, or draw arbitrary lines to halt its totalitarian trajectory.

It may have seemed possible, for a few post-war decades at least, to balance liberty against welfare. But Britain’s dour, down-at-heel arrival in the digital age has left us with these three ugly options. We can abolish the welfare state (politically impossible). We can pay a crippling surcharge in fraud (already unaffordable). Or we can accept the eye of Sauron (Do it for Our NHS!!!). In practice, the argument is already over; every browser cookie we accept, or Google search we perform, declares that we’re already staring into the Eye. We might as well make it official.

“The 2016 election cycle will be remembered for many things, but for those who work in politics, it may be best remembered as the year that political data reached maturity.”
— Andrew Therriault

In 2004, 22-year old Andrew Therriault wanted to get a Democrat president elected. “I got in my car and drove to Ohio,” he told me. “I walked around college campuses, signing students up to vote and all that.” But John Kerry did not get elected, and Therriault “wound up after the election, back at my parents’ house, broke, depressed and unemployed”. Talking to strangers about politics, he concluded, was not his forte.

Ten years later, Therriault found a job that did feel right: Director of Data Science for the DNC, the organising body of the US Democratic Party. By 2016, he was editing the O’Reilly publication Data And Democracy, from which the above quote is taken, describing in detail exactly how data can be used for political campaigning.

With hindsight, the 2016 election cycle was probably not best remembered as “the year that political data reached maturity”. It was, however, the year that many journalists and researchers discovered for the first time how data-driven, personalised political campaigning works, when they sought explanations for the political shocks of Britain voting Leave and the US voting Trump.

But Barack Obama had already won two Presidential elections using those techniques with increasing sophistication. A Guardian article in 2012 spared a single paragraph to privacy concerns between gushing about “a vast digital data operation that for the first time combines a unified database on millions of Americans with the power of Facebook to target individual voters to a degree never achieved before”, and the way, probably unconsciously, “the individual volunteer will be injecting all the information they store publicly on their Facebook page — home location, date of birth, interests and, crucially, network of friends — directly into the central Obama database”.

Despite the furore over Cambridge Analytica, and the supposed (but implausible) psychological manipulation of 2016 voters via social media, digital political campaigning remains the norm. Hardly surprising, in a world where we increasingly turn to personalised channels, and especially social media, for our news and commentary. How else could campaigners reach potential voters?

In the recent UK local and mayoral elections, the Labour Party spent over half a million pounds (£570,160) on advertising via Meta alone — that’s Facebook and Instagram. The Conservatives lagged behind with a mere £336,668, perhaps because their adverts tended to target pensioners who spend less time on social media: one with the headline “Tax the Codgers” was shown only to over 55s. Thanks to WhoTargetsMe you can now dig into the broad strategies of digital political campaigners, though not the details of how specific ads are targeted.

I asked Andrew Therriault how many data points he would have on a typical voter — dozens? Hundreds? Thousands? “Thousands,” he says, as if that’s obvious. But to think of that as a list of thousands of things he’d know about an individual voter would be misleading. Yes, it would include voter registration details — name, address, age — and perhaps answers previously given to surveys. But to that basic voter database Therriault’s team would add census data for the neighbourhood. Political campaigns also regularly use commercial data from data brokers such as Experian and Acxiom, collected from our interactions with businesses. Even though it’s hard to match data from those sources to individual voters, they can be used to build models, so what they do know about you can be used to predict things that they don’t know.

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Being able to profile individual voters helps a campaigner decide who to target, and tailor the message to them. There are two kinds of people worth targeting: those who might be persuaded to support your side, and those who need a push to get out and vote. “Persuade or mobilise,” as Andrew Therriault puts it.

Like other kinds of personalised advertising, political campaigns aim to show the most effective message to the right person, at the right time. And, also like other adverts, digital media allow the advertiser to monitor which ads are the most effective for this kind of person, by running trials of different variants to see which gets the best response. It is, in short, a huge feat of digital technology. It would be a mistake, however, to see the technology itself as driving this political change. Rather, the change from mass movements to “The Personal Is Political” predates the capacity to deliver a tailored message to an individual on a device that accompanies them everywhere.

The era of mass politics is not very old. In 1848, “the springtime of the peoples”, a huge Chartist demonstration in London tried to deliver a petition calling for universal male suffrage, secret ballots, and other reforms to parliament. According to the Illustrated London News, it took a cart drawn by four farm horses to carry the signatures in “five huge bales or bundles”. The government fortified all the bridges in London to prevent the assembled masses reaching Westminster, and went on to arrest the leaders and suppress the movement.

The road to every citizen getting a vote would be long and hard. In the US, race was the bar to democratic equality: in the UK, it was property, which excluded some men until 1918, and a third of women until 1928. In France, where all men did get the vote in 1848, women would wait until 1944.

But the ability to vote a government in — or out — was not the only form of mass politics. By 1874 around 10%  of the adult population of Britain were members of TUC-affiliated unions (mostly men). Organisations pressing for equal rights for women, or for non-whites in America, also demanded equality beyond the vote. However, by the time Martin Luther King made his “I Have A Dream” speech in 1963, the character of those mass movements was changing. Even as King called on the US Government to make good the “promissory note” to all Americans, black and white, guaranteeing “the unalienable rights of life, liberty and the pursuit of happiness”, that universalist vision was fragmenting and turning inwards.

Tom Hayden, later Senator Hayden, was in the immense crowd that heard King that day. After being inspired by Jack Kerouac’s On The Road to hitchhike across America, Hayden became a Civil Rights activist and spent some time in jail. There he drafted what would become the 1962 Port Huron Statement, a manifesto for Students for a Democratic Society:

“We oppose the depersonalisation that reduces human beings to the status of things,” it declares. “The goal of man and society should be human independence; a concern not with image or popularity but with finding a meaning in life that is personally authentic.” Politics, he said, should be a “means of finding meaning in personal life”, imbuing it with a therapeutic sensibility. The Port Huron Statement captures a moment at the beginning of a new political age. It talks of “loneliness, estrangement, isolation”, and “the felt powerlessness of ordinary people.” It matters not only that people lack power, but that they feel powerless.

The distinction between politics and personal feelings was fraying, and as the Sixties drew to a close, a slogan emerged to capture this new kind of politics. “The Personal is Political” is the title of a short 1969 article written by Carol Hanisch, an activist in the Mississippi Civil Rights Movement, and a founder of New York Radical Women, the group that protested at the Miss America contest in 1968. They were all women who were active in other movements — Civil Rights, Anti-War or the New Left — who wanted to discuss politics without men present. After the meeting ended, they kept arguing into the small hours at a nearby restaurant over apple pie a la mode.

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I asked Hanisch what she thought of the shift towards seeing the world in terms of identity. “It cannot be a positive change,” she told me. “We lose all sense of a need for unity to take on the source(s) of our oppression. Instead it invites us to escape the real world by “living in our heads”. It allows us to believe that if we can change ourselves and/or just a few people, everything will be fine, which is, of course, nonsense. We’re talking society-wide oppression here, not just individual attitudes. You can’t ‘identify’ yourself out of oppression, though over the centuries people sure have tried!”

Today, we take disagreement very personally. Not only issues that are entangled with our everyday lives, our feelings or how we see ourselves: even geopolitical issues or which political party we vote for are taken much more personally. People are less open to social connection with those who disagree with them politically. Survey after survey has shown that, increasingly, we are more likely to see those who disagree with us politically as closed-minded, selfish, hypocritical, immoral or lazy, and less likely to call them intelligent or honest. Negative feeling towards the other side, politically, has been steadily increasing since the Eighties.

I asked Andrew Therriault if he thought politics in general had become more personal.  “Yeah, from both the voters and the candidate side it’s become more individualised, in terms of even the basic facts we’re working with,” he says. “This idea that your political reality can be whatever you want it to be, and whatever someone else wants to tell you it should be. And there does not seem to be that sort of common grounding we once had when things were less personalised.”

Politics, in other words, is turning into a form of self-expression. It is no longer an arena to which we all bring our conflicting visions of how the world should be, and try to persuade others to join us in working towards ours — or even, in which we might listen to other people’s ideas and change our minds.

***

This article includes material drawn from Technology Is Not The Problem, published by HarperCollins.

Those essays and articles could be rolled into one easily enough, and sometimes it seems as if they have been. The formula is always the same, and can be usefully applied across the political spectrum. Start with talk of the “rising tide of authoritarianism” all over the world, as evidenced by “populism”, Brexit, Giorgia Meloni, Viktor Orbán, Justin Trudeau, Donald Trump, Joe Biden or any other leader you don’t like. Move on to explore how much of this “rising authoritarianism” is reflected in environmentalism, as evidenced by Just Stop Oil, Extinction Rebellion, the Green New Deal, the Great Reset, Bill Gates, Greta Thunberg or [insert name of bête noire here].

After this, list the historical inspirations for these new green authoritarians: Ted Kaczynski, Pentti Linkola and Dave Foreman should do for starters. Dig into the most miserable chans and Reddits of the internet and “expose” a few anonymised avatars promoting race war in the name of the planet. Mention the Christchurch shooter. Use the phrase “dark undercurrent” a lot. Chuck in the names of a couple of nature writers from the Thirties who became fascists. Mutter darkly about how Hitler was a vegetarian. Did you know there was an organic garden at Dachau? Makes you think, doesn’t it?

Having got here, you can move on to the meat of the thing: sombrely intoning about the “new threat to democracy” represented by this frightening movement. Depending on where you’re coming from, you can now explain how these new eco-authoritarians represent either a threat to our God-given right to drive, mine, manufacture, fly, burn oil and freely enjoy the glories that only Western Progress can provide, or a threat to diversity, equality, human rights, LGBTQIA++ people, refugees, “global justice” and a woman’s right to choose. Either way, the conclusion will be much the same: a non-specific but ominous call for more monitoring of “problematic” views, more work to tackle “radicalisation”, more laws to prevent protests and “hate speech”, and probably more internet regulation. For the safety of us all, of course.

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The problem, though, is that actual “eco-fascism” is notable mostly for its absence. Dark corners of the internet aside — you can find any craziness there, after all — it’s hard to find a single “eco-fascist” anywhere out in the real world. No public intellectuals, no writers, no philosophers, no politicians, no popular movements embrace anything of the kind. Plenty of people get the label applied to them of course — without the prefix, the word “fascist” has been a meaningless, all-purpose insult for decades — but they all reject it. I was in and around the green movement for a long time and I never met an eco-fascist, though I did have the pleasure of being called one.

So why all the dire warnings? I can think of two possible explanations.

The first is the simplest: there is something we can’t bear to look at, and we are trying to distract attention from it by screaming at the people who are pointing it out. The thing we are avoiding is the thing that we used to call “nature”, and the reality that we are trying to distract attention from is that we are part of it, we live inside it and that everything we do to it we also do to ourselves. Change the climate out there and it changes in here. Erode the soil, erode your soil. Poison the oceans, poison your culture. This is how it works, and this is what we are now facing.

And we cannot face it, even those of us who think we can. Whatever we think our politics are, we have no idea what to do about the coming end of the brief age of abundance, and the reappearance, armed and dangerous, of what we could get away with denying for a few decades: limits. Those who point these limits out — and who point out, especially, that the very existence of industrial modernity might be the root cause of the problems we currently face — can expect to be smacked down with the worst insults our culture can conjure.

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This is one explanation for the mysterious rise of the ghostly eco-fascists. But I think there might be another. The phrase “eco-fascist” is a label which is increasingly being applied to the wrong kind of environmentalist: those who offer up a vision of humanity and nature that involves roots, traditions, smallness, simplicity, a return to previous lifeways. They are contrasted with the right kind of green: those who are modern, global, progressive and — most important — friendly to the onward march of the technological society.

Nearly a decade ago, I wrote an essay called “Dark Ecology”, about the state of environmentalism. In it, I wrote about the emergence of a tendency in green circles which I called “neo-environmentalism”. The neo-greens — who preferred to call themselves “ecomodernists” — emerged as a reaction to the traditional green movement, which in its infancy had been relatively conservative, low-tech and focused on the human scale. The neo-greens rejected all this as backward, impractical and even dangerous. They believed, as I wrote back then, that: “Civilisation, nature and people can be ‘saved’ only by enthusiastically embracing biotechnology, synthetic biology, nuclear power, geoengineering”. The “new environmentalism”, they declared, in manifestoes like this one, would be, as we might now say, “grown-up”.

Sucks to be right, as the kids say. Since I wrote that essay, the neo-greens have, as predicted, mounted an effective corporate takeover of most of the environmental movement. Examples of what we might call Machine Environmentalism have been embraced by the corporate sector, big NGOs, global institutions and most of the intellectual class, most obviously in the “Green New Deals” that are popping up like summer daisies in all corners of the globe. Meanwhile, the green movement is splintering into camps, determined by attitudes to the kind of intrusive and novel technologies that the Machine Greens are pushing.

In Britain, this divide has been illustrated recently by attitudes to green pundit George Monbiot’s latest book, which embraces the neo-green vision. In the humbly titled Regenesis, Monbiot, an urban vegan intellectual, makes a case — based, naturally, on the “peer-reviewed science” — for the “end of most farming” and the replacement of much of its output with vat-grown, bacterial “food” manufactured via “industrial biotechnology”. The vast acreages of land which have been stripped of their farmers can then be “rewilded” in various Monbiot-approved ways, which mainly seem to involve growing forests for always-on urbanites to go wolf-spotting at weekends.

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In promoting a high-tech, globalised food system (perhaps overseen by the world government he has previously argued for), and casually calling for the destruction of the basis of post-Neolithic human civilisation, Monbiot offers a perfect example of what a neo-green future will look like: utopian, hyper-urban, technological, rational and most of all, “efficient”. What matters now, as he explains, is mathematics:

“It’s time we became obsessed by numbers. We need to compare yields, compare land uses, compare the diversity and abundance of wildlife, compare emissions, erosion, pollution, costs, inputs, nutrition…”

Welcome to what the greens have become.

A number of actual farmers who are also first-rate thinkers have taken aim at Monbiot’s Machine Green dystopia in recent months (Simon Fairlie, Chris Smaje and John Lewis-Stempel offer some of the best critiques), but while they might win the battle they are, for now at least, losing the war. Only last month, a pioneering Finnish “solar food” company championed by Monbiot was given EU permission to roll out production of their new “sustainable protein” (as part of the “European green deal”). The company says that the laboratory in which it produces this “novel food” — which it calls “Factory 01” — is part of a “food revolution” that will, for the first time in history, detach food production from the land, the farmers who work it and the culture it creates. Excitable admirers are already explaining that this may give us the ability to one day 3D-print our own food. I’m salivating already.

Older, crustier greenies like me, labouring under the yoke of a pre-modern sensibility which makes us reluctant to eat the sludge and live in the pod, might feel that something has gone terribly wrong with the numbers-obsessed rationalism that underlies this new, corporate-friendly green technocracy. But we have no five-point plan of our own, and we can’t peer-review our intuition, so our complaints don’t convince anybody who matters. And now that the localists, the deep ecologists, the peasants, the small farmers — and anyone else whose human-scale vision might interfere with the march of Progress — have been conveniently designated “eco-fascists”, we are able to behold the only legitimate form of environmentalism which remains: a globalised technocratic, “progressive” push for “sustainability”, led by intellectuals, entrepreneurs and professional activists.

The green movement, long ago co-opted by the Left, has now been co-opted too by technocrats. For this reason, the neo-green (or should we say soylent green?) food future can’t be viewed in isolation. It is only one aspect of the unfolding phenomenon which has been dubbed the “Fourth Industrial Revolution” — a revolution in which the Machine Greens, wittingly or otherwise, are playing a key part.

Dreamt up, like so many other catchy corporate catchphrases, by the World Economic Forum in 2015, the Fourth Revolution is a way of describing our historical moment. That same year, Foreign Affairs produced a book by the same name, to accompany the annual Davos gathering of politicians, business leaders and Bono. In it, the inescapable Klaus Schwab explains, in prose that could make a Martian invasion sound boring, the import of the times we are living through:

“The First Industrial Revolution used water and steam power to mechanise production. The Second used electric power to create mass production. The Third used electronics and information technology to automate production. Now a Fourth Industrial Revolution is building on the Third… It is characterised by a fusion of technologies that is blurring the lines between the physical, digital, and biological spheres.”

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The rest of the book, made up of offerings from various scientists, engineers, politicians and philosophers, explores the implications of this “blurring of lines” between created and uncreated, natural and artificial, wild and tamed. Now that we inhabit what the neo-greens like to call the “Anthropocene” — now that we are, in H. G. Wells’s formulation, Men Like Gods — what do we intend to conjure with the thunder and the lightning that pours forth from our just and rational fingers?

All of the contributors stress that overcoming the old-school distinction between the digital and the natural is the essence of the thing. Neil Gershenfeld, for example, defines the “digital fabrication revolution” — the one which will soon be growing our tank-bred bio-sludge dinners — as “the ability to turn data into things and things into data”. “Intelligent” buildings, wearable sensors, implanted chips: six years ago, when this book was written, these may have seemed radical. Today, it feels as if they have been almost normalised.

Partly this is because of the ubiquity of Amazon Alexas, Smartphone apps and endless, boosterish narratives about the exciting future that AI is building. And partly it is because the Covid pandemic was used as a trial run for precisely the kind of technologies — smartphone-enabled passports, digital population tracking, media-driven narrative control — which are now increasingly sold to us as a means of “saving the planet”. It is no coincidence that some of the loudest proponents of Machine Environmentalism were also fanatical supporters of the Covid biosecurity state. We are being trained to like what is coming — or at least to shrug our shoulders and accept its inevitability.

Perhaps the most important aspect of the Fourth Revolution, though, is what has been called “datafication”. The book’s chapter on “Big Data” explains that the knowledge available to each of us today on the Internet dwarfs that which would have been available in the Great Library of Alexandria, the greatest repository of learning in the ancient world. But size, as they explain, isn’t everything:

“Big data is also characterized by the ability to render into data many aspects of the world that have never been quantified before; call it ‘datafication’. For example, location has been datafied, first with the invention of longitude and latitude, and more recently with GPS satellite systems. Words are treated as data when computers mine centuries’ worth of books. Even friendships and ‘likes’ are datafied, via Facebook.”

Here we see the same “obsession with numbers” that George Monbiot demands of us as we contemplate how to produce our food and live in our landscape, and it reveals the elision between Machine Environmentalism and the elite-driven tech revolution it is part of. What we can see is that both achieve their goals through the process of datafication: the quantification of everything. The pattern of reality will be transformed into bits and bytes, comparisons and yields, numbers and statistics, until even novels and friendships and meadows and family meals on winter nights can be measured and compared and judged for their relative contributions to efficiency and sustainability.

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There is a rift here, and we should gaze deep into it, because there is something down there that we need to make out. It is the ancient rift between those who embrace the mindset of “datafication” — which is, in the form of sums and written language, one of the foundations of civilisation — and those who are repelled by it. I suspect it can never really be healed, because it marks the border between two distinct ways of seeing. The philosopher Jeremy Naydler has referred to them as ratio and the nous, but we could just as well call them left and right brain, mythos and logos, or — perhaps most simply — the sacred and the profane.

The Fourth Revolution and the Machine Environmentalism which it contains offer us a profoundly profane vision of the world. Life in this understanding is not a sacred thing but an engineering challenge. It is something which can be studied, quantified and constantly tweaked until we arrive at the most efficient version. This may be done with the best of intentions (or it may not) but the things which cannot be measured will, of course, be left out of the equation, and the things which cannot be measured happen to be the stuff of life. Love. God. Place. Culture. The profound mystery of beauty. A feeling for land or community or cultural traditions or the unfolding of human history over generations. Song. Art. They’ll “datafy” all of this soon enough, no doubt, or try to. But the kind of people who think that the Great Library of Alexandria contained “exabytes’ worth of information” rather than the collected fruits of hard-won wisdom are lost before they ever sit down to their datasets.

If you have ever wondered why climate change has so utterly dominated the green debate to the exclusion of so many other problems which stem from industrial society — mass extinction, soil erosion, the collapse of human cultures, ocean pollution — then the answer, I think, is here. Climate change is a problem amenable to numerical questions and technocratic answers. It is, furthermore, a problem which, almost by definition, can only be solved by elites. If you can’t read or understand the “peer-reviewed science” then you are open to being intimidated into fearful silence by those who can, or claim they can. And those people — drawn, as all green “thought leaders” are, from the upper strata of society — will bring with them a worldview which treats the mass of humanity like so many cattle to be herded into the sustainable, zero-carbon pen. If you’re wondering where you’ve heard this story before, just dig out your dirty old Covid mask. It will all come flooding back.

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Big Tech's threat to democracy

By Matthew Crawford

Interestingly, some of the progenitors of the Fourth Revolution are themselves uneasy about where some of it is leading. Even Klaus Schwab, who these days is often lazily presented as a volcano-dwelling Bond villain pulling the global strings, admits to unease at the speed and scale of change. In the book, he expresses his timid concern about how our “quintessential human capacities such as compassion and co-operation” might be eroded by deep shifts like these. “It is already changing our health and leading to a ‘quantified’ self,” he writes, “and sooner than we think it may lead to human augmentation”.

Even as he proselytises for the Fourth Revolution, Schwab can see what is coming. Google maps and smartphone apps were always just the beginning. We are headed into a Brave New World of all-knowing Smart homes and vat-grown sludge for breakfast, and every step along that road will make perfect rational sense. A Panopticon world, remade at the nano level by the allegedly well-meaning, lies just around the corner. C. S. Lewis understood the trap well: “Of all tyrannies,” he wrote, “a tyranny sincerely exercised for the good of its victims may be the most oppressive… those who torment us for our own good will torment us without end, for they do so with the approval of their own conscience.”

What the Fourth Revolutionaries never seem to grasp is that the question you ask will frame the philosophy you develop. If your question is “how can we remove all this atmospheric carbon to avoid what these computer models say is coming?” then the answer can only lead you to a globalised technocracy. If, on the other hand, the question you ask is “how can we build lives which offer us meaning, in alliance with the rest of nature?” then you may be led in a very different direction.

It is the first kind of question that a Machine society will always ask, and there will always be endless, multiplying justifications for asking it. Ecology, equality, feminism, democracy, public health, growth, security, the war on terror or crime or drugs or whatever: there is always a reason for Big Data. The control is always necessary to prevent a greater evil. Even a movement which once challenged this grim way of seeing has today been bought and sold by it.

For at least 200 years we have been thoroughly undermining the foundations of all of our assumptions. Now, new cracks in the masonry are appearing daily. Can we caulk them up with vat-grown eco-sludge and hope they don’t spread? Can Big Data come to our rescue? What can we measure, manage, monitor, to help us escape from this? This is what I think, though I often wish I didn’t: we are living now through what may be the final triumph of Rational Man. The tower he has made has nearly touched the very roof of the world. Every old story can tell us what will happen next.

Over a century ago the director Sergey Eisenstein decreed that, sinceindividualism was horrendously bourgeois, the heroes of Communist film and theatre should be the masses. He created remarkable crowd scenes in films like The Strike, in which “the people” moved in unison like one homogenous being: a collective revolutionary energy. At the CCP’s birthday party, it was the scenes depicting the early twentieth century that notably nodded to early Soviet cinema, with relentless torrents of actors rushing from different angles across the vast stage, coalescing first into an arrow, then into a star.

Interspersed between the crowd scenes were vignettes about Chinese Communist leaders — done in the stiff, declamatory, hysterically happy style of Stalinist Socialist Realism, which replaced Eisenstein’s avant garde ideas as the USSR wore on. Mao was shown founding the CCP with grandfatherly goodwill surrounded by fiery-eyed comrades reminiscent of Petrograd’s proletariat.

After over two hours, history caught up with the present. Ballet dancers dressed in the uniforms of the People’s Liberation Army pranced with machine guns, celebrating modern China’s military might. A huge screen showed videos of Xi Jinping giving speeches at the UN, in that vacant, bored way of his, like a hyper-wealthy schoolboygoing through the motions in class knowing he’ll never have to work. Every clip of the President was greeted with high-pitched squeals from the audience, of the sort associated with boy-band concerts: Stalinist cult of personality meets Instagram era energy.

The political message behind the aesthetics was ripped from Soviet propaganda, then: our system achieves success when the masses are directed by a few superior leaders in the Party. The birthday celebrations were another opportunity for CCP flunkeys to restate their favourite mantra: liberal democracy is a mess; checks and balances have degenerated into confusion and paralysis; centralised control is the way forward.

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The history of the twentieth century seems to show that this is bunk. In the Cold War, Politburo control lost heavily to the messy but more open approach of liberal democracy. But the CCP is betting that something fundamental has changed. At the climax of the birthday performance, instead of celebrating some Soviet-style Five Year Plan, a great neon blue “5G” hovered above the stage, while hologram ones and zeros drizzled down. The compere, holding a red book, celebrated how China will lead the world in the online era.

“Tech advances” the Sinologist Martin Hala explained to me, “offer an opportunity to leapfrog ahead, and at the same time to implement the “original intent” of Leninism and Maoism, mostly through facilitating centralised control that had previously been difficult to achieve.”

Centralised control was prevented by the primitive technology of the twentieth century, the theory goes, but in the age of all-knowing algorithms, AI and ever-bigger data, the force that can amass the most information and then use it to direct society wins. The CCP envisages a world of enhanced online surveillance, where all your behaviour is tracked and analysed, where the vestiges of privacy are wiped out — but which will deliver success and convenience, even satisfaction. Total digital surveillance will allow the CCP to know so much about you — much more than you can know about yourself — that it can deliver you the optimal education system; the finest health program; the most appropriate job; the safest, sleekest city; and the ideal entertainment. Even the “free” market can ultimately be controlled more effectively, the CCP argues, through centralised big data analytics about where that market needs correcting.

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Such visions of the future are accepted, whether happily or unhappily, way beyond Beijing. No less a sage than Yuval Harari believes that today’s “technology favours tyranny”. Implicit in such thinking is a concept of the human as reducible to data. Luciano Floridi, a professor of all things Internet, Ethics and Philosophy at Oxford, argues that the digital era signals a revolution in what it means to be a human. Just as the Copernican revolution made people realise we are not the centre of the universe, and the Freudian one that our thinking selves were actually impelled by our unconscious, so today we need to realise that we are just sub-sets of our own data, that our data knows more about us than we do. The Cambridge University Psychometrics Centre has suggested that our data can predict everything from our sexuality through to our real political preferences. Their studies claim that our digital footprints “are better judges of personality than friends and family”.

This thinking also pervades Silicon Valley. For the tech monopolists, more data means more profits rather than direct political control, but the underlying premise is the same as the CCP’s: we suck your data, and then feed you the “optimal” products, (fake) news, and friendships. Who needs complex freedom of choice when algorithms can decide everything for you? There’s a whole allegedly philosophical strain among the self-appointed “futurists” of Palo Alto, with their dreams of data collection enabling technology to surpass the humble human mind in what they term “singularity”: the moment that machines will think better than humans. “Singularity, Not Self,” read a big piece of somewhat CCP-style bit of corporate graffiti in Facebook’s London offices in 2019.

The internet, in short, is the interface on which familiar great questions about governance and what makes us human are playing out. The challenge China and parts of Silicon Valley are throwing down is fundamental. In a digital age, is democracy really the best system? If our data knows more about us than we do, then isn’t it best to hand decision-making to a higher power? Comparisons with the Cold War are generally unhelpful when we consider our interconnected world, but there is a battle of ideas starting to play out. As the new US administration and the leaders of “Global Britain” mumble pleasantly about reviving democracy, this is where to start.

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“Ours is a spirit that goes freely through the world and inspires others to join in the cause of defending liberty,” says the US President, though it’s frontline states like Estonia and Taiwan who are showing the likes of US and UK the way. In Taiwan, Audrey Tang — the whizz-kid activist who became Digital Minister — is the great apostle of making sure tech can favour democracy, not tyranny. The model of Silicon Valley social media is often built around grabbing your attention (and thus your data) by encouraging behaviour that is divisive and hateful — and ultimately undermining democratic discourse. Tang, in contrast, uses new platforms that strengthen deliberative debate. Pol.is, for example, is designed to foreground the commonality between people’s arguments on divisive policy issues, and then shows how they can move to consensus and create laws acceptable to the majority.

Imagine if, instead of the rancorous shit-show of our current social media, we actually had public service platforms that created the online iteration of the ancient Greek Agora. It would actually give people a stake in political decisions on everything from the local budgets through to reforming the health system, rather than letting them feel “left behind”.

Tang also argues that we should stop lumping all “data” together. There are some things, like health, where it is useful to aggregate our behaviour in order to design the most efficient interventions. Citizens can opt to hand their health data to a central institution, as long as it is then not used for any other purpose. It’s no accident that Taiwan had one of the best responses to the Covid crisis — with relentless tracking of cases, but nothing like the totalitarian measures taken inside China. But choosing to hand over health data doesn’t mean you have to hand over everything else.

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Because ultimately, autonomy matters. In Joanna Kavenna’s 2019 novel Zed, adystopian dark comedy, the future currentlyenvisaged by Silicon Valley and the CCP has arrived. Society is ruled over by tech companies who track and monitor all our behaviour, and then spit out the “ideal” lifestyles, jobs, policies and romantic partners based on predictive algorithms. But the algorithms don’t actually understand humans. The data spits out false outcomes. The romantic partners are never right. The “ideal” society makes people depressed and mad.

Every generation has to re-define what it means to be “free”, where our sense of self begins. In a digital age our individuality begins at the place where our data cannot understand us. Freedom emerges in the space between the algorithms and our actual lives. Tech can deliver many wondrous and terrible things, but it will always fall short of really knowing what makes us human.

Sound too good to be true?

Other countries are already using contact-tracing apps. Contact-tracing is also used in sexually transmitted infections, where previous partners may be contacted with advice to get themselves a test. But whereas we tend to remember our sexual partners, we tend not to remember everyone who shared our train carriage or served us in a shop several days before we had symptoms. That’s where technology could help.

I write constantly about the threat to privacy of letting our smartphones share data that reveals where we go, what we do, and who shares our personal space. And although these are exceptional circumstances, we should not stop valuing our privacy. Emergency measures have a habit of becoming the new normal. And information about who we’ve been close to could be of interest to all sorts of people, from blackmailers to over-enthusiastic police officers enforcing their own interpretation of “necessary activities”.

Let’s compare some of the contact-tracing technology already in use, to see how the UK could avoid the pitfalls while harnessing our phones to battle the virus.

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The Chinese app, AliPay HealthCode, raises some red flags. It assigns users a unique QR code which displays red, yellow or green, indicating your health status, and which determines how much freedom of movement you’re permitted. How that risk category is calculated remains opaque, though it uses proximity to known infected individuals or hotspot locations in that calculation. It sends your identity and location directly to a server accessible by the police, who can use it to enforce the quarantine demanded by your colour status. Use of the app is not compulsory, but even local movement may be impossible without it.

South Korea uses existing sources of data when tracing contacts, rather than a specific app (though they do have an optional app to monitor infected people in mandatory quarantine). They use interviews, supplemented with CCTV footage, smartphone location data, and even credit card records, to reconstruct an individual’s pre-diagnosis activities. They have been criticised for making public the locations and activities where others may have been infected, which has led to individuals being publicly identified, and deterred others from getting tested.

Israel has already allowed its internal security agency, Shin Bet, to access cellphone geolocation data, and that can now be used for coronavirus tracking. Cellphone users who have been close to known infected individuals may receive text messages telling them to self-isolate. However, Israel’s Association of Public Health Physicians criticised the project, asking why no health professionals were involved in designing it. The Ministry of Health then launched a location-based app, Hamagen, on an opt-in basis, also using GPS data.

Singapore’s TraceTogether app supplements interview-based contact-tracing in a similar way to South Korea’s. TraceTogether doesn’t collect data on where you have been, because it only wants to identify who has been within infectible range. To do this, it uses Bluetooth.

Where geolocation-based apps use the cellphone’s GPS function, and its connections to the nearest cellphone towers, Bluetooth exchanges signals, not with a cellphone network, via a tower, but with other Bluetooth-enabled devices within a few metres.

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Good news if you want to identify people at risk from the cellphone’s infectious owner, without handing over to a central database lots of personal data about where you were. All the app needs is Bluetooth’s Relative Signal Strength Indicator readings to approximate how close you were to another device, and for how long.

As the app’s ‘How Does TraceTogether Work?’ page puts it: “This proximity and duration information is stored on one’s phone for 21 days on a rolling basis – anything beyond that would be deleted. No location data is collected.” The data stays on each person’s phone unless they test positive for Covid-19, in which case they’re asked to hand it over to Singapore’s Ministry of Health (MOH).

The MOH works with them to trace their contacts for the previous 14 days. There’s a legal requirement to help MOH map recent activities if requested, including producing other data held on your phone in other apps. But it’s not compulsory to use TraceTogether at all.

Several other countries are introducing apps modelled on this app: the pandemic has spurred a worldwide rush of innovation and technical experimentation.

Most of us would welcome the chance to contain the spread of the virus without having to stay locked down until a vaccine arrives. But do we have to trade our privacy by turning everyone’s phone into a tracking device? Perhaps not.

More than 25 scientists across eight European universities decided that, rather than wait for others to produce bad apps, they would write their own. Or at least, write the basic code that others could use, and make it freely available. And the result was DP-3T.

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Like Singapore’s TraceTogether, DP-3T is designed around Bluetooth’s capacity for device-to-device communication. Like TraceTogether, it transmits and receives ID codes that can later be used to reconstruct whether a sick person has been close enough to infect you. But it has some important refinements that mean no centralised body ever needs to know who has been close to whom.

Instead of transmitting codes that can be linked back to the person using the app, a DP-3T app transmits a random jumble of letters and numbers that changes regularly. Other devices receiving the codes won’t even know if this is the same device as yesterday, which is an important layer of protection for anonymity. Each device running the app will store all the codes sent and received.

If one person does get a diagnosis of Covid-19, they get a passcode from the healthcare professional making the diagnosis, which allows them (just once) to upload their “sent” codes from the pre-symptomatic contagious period to a database. This gives the database no information about the patient’s identity, location, or anything else, just the codes transmitted during the relevant period.

All the other phones running the app have access to the database of codes, but only to cross check against the codes in their own “received” file. The app can do this regularly and automatically, and alert the user if the level of exposure means they should self-quarantine, or get tested.

Everyone keeps control of their own data, the centralised database contains only anonymous codes from infected people, and nobody has the whole picture. It is possible to have both contact tracing and privacy.

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Is this the basis for the NHSX app being developed, which is also designed to use Bluetooth to track proximity?

Probably not.

“All data will be handled according to the highest ethical and security standards, and would only be used for NHS care and research,” according to Hancock’s announcement, “and we won’t hold it any longer than is needed.” But this implies that data from the app will be uploaded to a central database, where it can be used for other purposes. The fact the government felt the need to say that the police would not have access to the data suggests that it won’t be anonymous.

The public’s appetite to put the app on their phones will be reduced even further by the revelation that NHSX has teamed up with Palantir, a data-processing giant that works with the CIA (which part funds it). Palantir will be processing, not controlling the data, but our government has a patchy record on transparency over data retention and use.

However, a few developments over Easter weekend could force NHSX to radically rethink their project.

On Good Friday, Apple and Google released a joint statement, in itself a sign of the unprecedented times we are living in. They plan to design contact-tracing technology, either apps or the digital infrastructure on which apps can be built, which looks very like the DP-3T model. The two tech giants also announced another change that sounds like a minor technical detail, but could be more significant for the NHSX app. It concerns the way Bluetooth runs on Apple, and newer Android, devices.

When I asked a Singaporean friend about the TraceTogether App, he told me, “It’s a pain to use,” because it needs Bluetooth to be running all the time. But because Bluetooth is a digital blabbermouth, dishing out your data to any local device that wants to know, Apple devices don’t allow it to run in the background while you’re doing anything else, or while your phone is locked. Google’s newer Android systems run along the same lines.

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This means that if you want the TraceTogether app to work “you have to leave it on and … ‘upside down’ in your pocket to go into Low Power mode,” says my Singaporean app user,  “but of course everything’s haywire if you leave your phone unlocked in your pocket as you walk around!” At best, you pocket-dial your ex. At worst, a pickpocket has access to your entire life.

Dilemma for Google, and especially for Apple. It makes a big point of privacy, and of keeping your data on your device instead of on somebody else’s database. Could it allow Bluetooth to run when the app is not active, but for approved Covid-19 apps only?

This in effect, is what it has said it will do. But its definition of approved Covid-19 apps is drawn more narrowly than many expected. In fact, Apple devices will only allow Bluetooth to run in the background for more privacy-friendly, decentralised contact tracing systems, like DP-3T.

The solution to the Bluetooth privacy problem is that the exchanging and storing of randomly generated codes will happen, not in an app, but securely within the operating system. Data will leave the device only for explicitly authorised uploading to an approved database. The only other interactions possible with external databases will be queries about matching numbers. The app could potentially export any risk scores it has calculated for you, but not the codes sent and received by Bluetooth.

It sounds like a minor detail. But it could be a big spanner in the works for the NHSX app, if it doesn’t conform to that decentralised model. The app wouldn’t work with the phone locked or while you are using it for anything else. This inconvenience has been a big barrier to uptake in Singapore, where few people are using it, certainly fewer than the 60% of the population that public health experts have estimated as the minimum needed to make it work.

All these developments will have taken the NHSX project by surprise. It now faces a choice between carrying on with an app that’s less convenient to use, and that fewer people will trust, or starting again with a different model, delaying rollout, and denying them access to data that could be useful for research and for planning.

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In the long run, however, adopting the DP-3T model has huge advantages. It makes it easier for lots of us to download and run an app, without worrying about whether we’re releasing data to big corporations and government bodies to use for other purposes. It avoids setting precedents for centralised databases of who we spend time with. And it’s the foundation for internationally-compatible anti-Covid-19 digital solutions, which we may need for some time to come.

So, quick redesign, and Matt Hancock can roll out the app and get the UK back to normal life in a couple of weeks?

Sadly, it’s not quite that simple.

Allowing app users to self-diagnose opens the door to false alarms by hypochondriacs (or schoolkids who aren’t happy their school reopened). Medical validation needs readily available Covid-19 tests, so that professionals can authorise uploading the codes. Society needs to facilitate people going into self-quarantine, getting tested themselves, and locking themselves away for weeks, without delays. And a majority of the population needs to be using the app, but not everyone has a smartphone equipped with low-energy Bluetooth.

Automated alerts can’t replace detailed contact tracing, as practised in South Korea and Singapore. But interview based contact-tracing is labour intensive. Contact-tracing tens of thousands of cases individually would be an immense task for a health service that can’t even pick up every 111 call.

However, a widely used app in conjunction with testing could be a workable compromise, reducing the transmission rate to a scale that the NHS can handle, while allowing people for whom the risk is acceptably low to return to work, and to a more normal life.

How low a risk is acceptable, and how normal life should be, are political questions. No app can answer those.

I will soon arrive, with some relief, at the glorious destination of being completely uninteresting to men. It’s called old age.

I know about misogyny, then — all women do. I know how it summons wreckage in women’s lives. I am poorer because I am a woman, I have lower status because I am a woman, and I am less confident because I am a woman. Although I am brave in print, in life I squeak like Minnie Mouse, and I wish I didn’t. I have had sexual experiences I did not want because I am a woman: and still, I know I’m lucky. I am a rich, white, western woman. Some people call it privilege. I call it the thick end of the wedge.

But I did not know how extensive — how absolute — the web of misogyny is until I read Invisible Woman: Exposing Data Bias in a World Designed for Men by Caroline Criado Perez. I read it while shouting at my husband. Perez is a sensible, second-wave Feminist — not a fourth-wave imbecile who thinks that prostitution is a valuable human right to be defended by those noble activists previously known as pimps. She campaigned for a statue of Millicent Fawcett to be placed in Parliament Square and a picture of Jane Austen (a sanitised portrait in which Jane looks pretty, because you can’t have everything) to be placed on a bank note.

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I read the book in a rage. What is sensed, and lived, by women in a patchwork across class and continent is presented, in Criado Perez’s plain, detailed and almost overwhelming prose, as a self-perpetuating and all-consuming system of oppression and denial, in which women are an appendage to men, who have designed the world entirely for their own comfort and safety. There is no anger in Criado Perez’s pages — she is too busy with evidence for that — just as there is, as far as I can tell, no malice in this vast system of misogyny. It was all done thoughtlessly, like a man running over a child in the dark. The truth is, they just didn’t see us.

The book opens with Simone de Beauvoir’s words: “Representation of the world, like the world itself, is the work of men; they describe it from their own point of view, which they confuse with the absolute truth.” Criado Perez then spends three hundred pages proving de Beauvoir right, in chapters on environment, workplace, design, health, and politics.

Almost everything is designed for men. Cities are designed for men. Transport infrastructure is designed for men: when, for instance, did a wise woman last take a baby on the London Underground, which is clearly, during rush hour, unsafe, and yet usually a woman’s work? Why is money tossed at road-building, which favours “the unencumbered man”, not provision of public transport, which more women use on their longer, more potted trails through life? Even parks are designed for men, for unless care is taken to design them for women’s safety, fewer women — and girls — will use them, and their health will suffer.

One fascinating anecdote tells how, when the toilets of a theatre were made gender neutral, it only increased men’s space, for they used the cubicles in both, while women were, due to their bodies, denied the urinals. Women spend 97 billion hours a year, she writes, looking for a safe place to relieve themselves, and this is likewise detrimental to health. Invisible Women is full of such maddening insights.

The generalisations are accurate — women earn less, have less, care more for children and the elderly (which is spun, deceitfully, as “choice”) and slip into invisibility as the system devalues them. The would-be powerful woman — Hillary Clinton, for instance — is struck down for her presumption, and replaced with a dangerous imbecile. But it is the detail that compels.

For instance, in Britain, the Pregnant Workers Directive does not apply to politicians; there is, Criado Perez writes, “no provision for them [female politicians] to vote without turning up in person”. For politicians! In July 2018 the Conservative MP Brandon Lewis was paired with the pregnant, and absent, Liberal Democrat MP Jo Swinson. But he forgot, and voted anyway, and he got away with it. Apple, in 2017, declared that its US HQ was “the best office building in the world”. It had a spa and a dentist. It did not have a creche.

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I would describe the condition of women in the workplace as a shit storm. People who believe they are meritocrats are the least likely to be meritocrats. Universities and tech companies who boast of being ‘woke’ are among the worst employers. Bias against women is everywhere – see the archetypes — and without care and research, it thrives. Blind auditions for orchestras raised female representation by 45%. In academia, double-blind reviews (where the author and reviewer are anonymous) mean that female authored papers are more likely to be accepted, and better rated. For women to thrive as men do, their gender must, quite literally, be masked.

This bias leaks into the schoolroom — where girls learn that scientists and geniuses are male; misogyny is not inbuilt, but learned. Algorithms, invented by men, favour men. We corrupt children, and we corrupt computers, and, in turn, they corrupt us.

Every victory contains a defeat. Female soldiers are injured off the battlefield because they are forced to march in step with men. Packs that took breasts into account were not invented until 2011, 35 years after women were admitted to US military academies. Female coastguards in the UK cannot easily use the toilet on duty — see the 97 billion hours above — because the clothes are not designed for them. In 1997, a female police officer died in the UK because she removed her body armour to operate a hydraulic ram, which she could not use while wearing body armour that did not fit her because it was designed for a man.

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There is too much death in this book — some caused by sexual assault, because women’s spaces are unsafe or non-existent, and some caused by negligence, because women’s bodies are forgotten. There is so little investment in pelvic floor health, for instance – men decide what is suitable for research, and they forgot the female pelvic floor — that a current scandal details how women with prolapsed vaginas had mesh inserted as a cure. Many were left with chronic pain. At least one, in Scotland, has died. Women seeking help for physical problems are dismissed as fantasists, or trouble-makers. Viagra is plentiful. So is death in childbirth.

It’s comic sometimes, if you care to laugh at darkness. The voice command software in a Ford Focus did not respond to the female driver, but it did respond to her husband in the passenger seat — because it was programmed to hear a deeper voice. Criado Perez lost me only once, when she noted that the average smartphone is too small to fit easily into a woman’s hand. Not important, I thought, but then she detailed a riot in which a female researcher could not take a photograph with one hand, while a male one could. It’s a tiny thing, but it brought disadvantage, glibly.

This is a brilliant book, then, to thump the gropers and the deniers with — the people who point out masculine suffering, which is real, to detract from female suffering, which is likewise real. Everyone should read it.

That was the year the UK voted for Brexit, and the US voted for Trump, so yes, you could say that we saw major political changes. But Foroohar falls into the trap of seeking explanations for the status-quo-shaking votes of 2016 in technology, instead of asking what has changed in politics. There’s a whiff of disillusionment from somebody who thought technology was going to change everything for the better, and then doesn’t like the changes that actually happened.

Whether good or evil, Foroohar seems to ascribe supernatural powers to technology, or at least to the companies that create and provide it.

Let me backtrack for a moment. This is a highly readable and thoroughly researched book. In particular, it’s good to learn about the business side of Silicon Valley from somebody who has spent years reporting on the big companies whose businesses make our business their business. Foroohar even turned poacher for a while, working for a dotcom startup shortly before the first bubble burst at the beginning of this century, before going back to being gamekeeper at the Financial Times.

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It makes a refreshing change to look at the FAANGs — Facebook, Amazon, Apple, Netflix and Google — not as magical communities of wizards (good or evil) creating technology in some kind of other-worldly, post-material universe, but as companies that employ people, sell stuff and seek profits.

It’s useful to explore in what ways they’re just like the dominant industries of earlier eras, the railway or oil companies that formed monopolies, lobbied politicians for regulation that would hamper their competitors but not them, and discouraged their employees from organising for better lives.

It’s also useful to look at what’s different about Big Tech. Google’s chief economist, Hal Varian, describes how the importance of data to Silicon Valley rests on capturing relationships with consumers in digital form, enabling companies to both access and understand us on a mass scale. This lends itself to a new form of natural monopoly, as we gravitate to platforms that are more popular, concentrating data about us with a few big companies. This in turn advantages those companies, and the bigger they grow the more they can simply buy up the competition, as Facebook did to Instagram and WhatsApp.

But sometimes Foroohar’s description  of what’s changed sounds overblown, even mystical. “A smartphone’s powers are both unknowable and immensely powerful in the way that magic, by definition, is,” she asserts, contrasting the new tech to old tech such as cars, light bulbs and telephones. But hang on, isn’t electricity also “silent and invisible”, as she puts it? How many of us even today could explain how electricity is generated, how a landline telephone connects us to somebody many miles away, or even the workings of an internal combustion engine? And as for the financial structures that keep the amps flowing and the automobiles rolling, they are as opaque as they have always been.

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True, the business models of Google and Facebook, which provide free services to millions of users while getting their income from selling adverts, are not the same as railroads and oil companies, which made their money by selling goods and services to the people who used them. But they’re not utterly different from magazine publishers whose real profits came from advertisers, not the cover price.

In fact, the more I read, the more I felt that Big Tech is not as different from past industries as it would like us to believe, or as Foroohar appears to have believed until her recent disillusionment.

Interviewing the former Google Chief Ethics Officer, Tristan Harris, she tells us: “He saw something that most people ‘on the inside’ didn’t: that we had reached a tipping point in which the interests of the tech giants and the customers they supposedly served were no longer aligned.”

I have two problems with this revelation. First, the users of free services are not the customers. Remember the aphorism — if you’re not paying for the product, you are the product. We users of free internet platforms, apps and email are served only in the sense that a buffet is served: on a platter, pre-sorted for easier targeting by advertisers. We should never have expected our interests to be the priority of these platforms. Dinner tables are not designed for the comfort and happiness of the sandwiches.

And second, even from the point of view of the real customers, the ones paying to advertise to us or to access data about us, who would be naïve enough to assume that the customers’ interests are aligned with the interests of the sellers? True, most companies want satisfied customers who will keep coming back, but one side wants to turn a profit and the other to bag a bargain.

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“Their goals are not your goals,” says Harris, perhaps revealing more about the unworldliness of tech company insiders than about the constant nature of capitalism. Call me a cynical old Marxist, but nobody should be allowed to leave home before understanding that life is a constant process of negotiating conflicts of interest, and that no tech company is going to love you like your mother does.

Bringing us back to ‘2016: the year it all changed.’

Foroohar has already told us some of the history of how technology was designed to keep us online and interacting for as long as possible, using behaviourist theories of human psychology. She has also told us the story of her 10-year-old son, bamboozled into spending a lot of (her) money while playing FIFA Mobile, notching up $947.73 on one credit card bill. “I couldn’t help it!” he tells her, “… the game just kind of took over.”

“He described a kind of brain fog, a trance, in which he simply lost himself.”

—

So what changed in 2016?

Not the way Big Tech is involved in political campaigning as well as commercial advertising. Foroohar herself notes that politicians have been using data and marketing techniques for years, and that Facebook, Twitter and Google played important roles in the 2012 US presidential election (working for both sides). She could have pointed to UK examples too, as all major British political parties borrowed strategies, software and people from successful US campaigns.

What changed seems to be Foroohar’s own attitude to how Big Tech is used. Methods that Barack Obama and Hillary Clinton used became “dark”, “dirty” and “corrupt” when Donald Trump’s campaign uses them. She describes the Facebook investor Roger McNamee as being “shocked, as were most people, by the outcome of Brexit”. Which is a bizarre way to describe a majority vote on a high turnout. We can probably assume that more than 17 million British people were not shocked, though they might have been pleasantly surprised to find their Leave votes on the winning side.

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There is much to criticise about the way data and social media platforms are used in political campaigning. Microtargeting, in particular, allows political parties to evade arguing for principles that could unite a majority of voters, by instead sending different messages to different voters, tailoring their policies for niche audiences. More broadly, the reduction of politics to a marketing exercise, using the same tools as those that sell us soap or shiny gadgets, is a travesty of democracy. That’s why a few of us started raising the alarm well before 2016.

But because Foroohar points the finger at technology, instead of politics, her remedies are technical, not political. Ironically, they involve asking those same tech companies to control political messaging, and to take responsibility for political campaigns.

Much as it pains me, I agree with Mark Zuckerberg, who said (in a statement that Foroohar calls “truly stunning”): “I find the notion that people would only vote some way because they were tricked to be almost viscerally offensive.”

Foroohar takes seriously the challenge of Big Tech companies, and the monopolies they hold not just in services but — through data — in relationships with us, the public. She calls for more discussion by democratic governments about what can be done to defend the interests of citizens, rather than narrowly defined consumer rights. There is much to learn and discuss in this book.

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But as she looks to the same powerful companies to exert more control over how democracy works, I am reminded that her model for the pernicious influence of gadgets and games designed to bypass our rational selves is her 10-year-old son. Children don’t (so far) have the vote, precisely because we don’t expect them to have the necessary powers of judgment and self-control. We also don’t let them buy houses or drive cars, sign contracts or have their own credit cards (which is how Foroohar ended up with that $900 bill).

But we are not children. If we let ourselves be bamboozled into voting for something we don’t believe, that’s our responsibility. If politicians seek to address our fears and resentments instead of our hopes and our capacity to weigh up arguments, it’s those politicians who should be held to account, not the platforms they use to find and target us.

And we should know better than to blindly trust a big company to do what’s best for society, just because its services are free, its founders are cool, and it once had the motto, “Don’t Be Evil.”

Anyway, the story goes that a military AI was trained to detect tanks in photographs. It got shown lots of pictures, some with tanks in, some without, and it was told which was which. It worked out features that were common to the tank-containing pics, and then, when given a new picture from the same source, would use that info to say “yes, tank”, or “no, no tank”, as appropriate.

But apparently, when the AI was given pictures from a new source, it failed utterly. And it turned out that the AI had worked out that the photos with tanks in had been taken on sunny days, and the others on cloudy ones. So it was just classifying well-lit pics as “yes, tank”. When new pictures, taken by other sources which hadn’t been photographing sunbathing tanks, were used, the system broke down.

The AI blogger Gwern has tried to trace the story back, and it transpires there are multiple iterations of it: sometimes it’s saying tank vs no tank, sometimes it’s identifying Soviet vs American tanks; sometimes it’s ‘sunny days’ that’s the confounding factor, sometimes it’s the time of day, or that the film had been developed differently. Versions go back at least to the 1980s and possibly to the 1960s. Sometimes it’s Soviet tanks in forests, sometimes it’s Desert Storm.

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There another story about an AI set a task of telling huskie dogs from wolves. All the wolves in its training data were photographed on snow, so the AI learnt to call any animal photographed against snow a wolf. In this real story, the AI was deliberately badly trained, on deliberately badly chosen training data – it was a test. But when it was trained properly, it worked much better

This story is used to make the point that any AI is only as good as the data you train it on, and it is impossible to know how good the data you’re training it on actually is.

While this may be something of an oversimplification, it’s essentially true. The reason why AI is useful – or machine learning software that uses neural networks, which is usually what people mean by AI – is that it can work through absolutely vast amounts of data and find patterns and correlations that humans can’t. No human could go through that much information.

This capacity has profound impacts. In science, the huge amounts of data that are thrown up by, for instance, biomedical research, or astronomy, can be analysed to reveal previously unexpected links: genome-wide association studies found that multiple sclerosis, is in fact, a disease of the auto-immune system, like rheumatoid arthritis, even though it presents as a neurodegenerative disease like Parkinson’s or Alzheimer’s.

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But the trouble is that – pretty much by definition – it is impossible for a human to check those datasets. If a human could check them, you wouldn’t need the AI. And if a dataset is, for some reason, imperfect, then the AI will learn things from it that you won’t want it to. It won’t learn to say there’s a tank in every picture of a sunny day – modern image-recognition software is cleverer than that, and trained on much wider datasets, and there are ways around that sort of problem anyway – but it may have analogous, but more subtle and perhaps more insidious, problems.

That’s fundamentally the worry about AI being racist, or sexist. You might train your AI on some dataset of people who previously performed well at a job, or people who have or haven’t reoffended after release from prison, and those datasets contain large amounts of information about each person; years of previous experience, say, or number of previous offences – but also age, sex, ethnic makeup.

And if the training data tended to favour people of a certain sex or race, then the AI may learn to preferentially pick those people as well. It doesn’t even matter if the data isn’t the product of people being racist or sexist themselves. The training data may be full of people who really did do well at their job, or not reoffend – analogously to the training data correctly labelling whether or not a picture is of a tank.

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Then it may turn out that, for societal reasons, women or black people turn out to do, on average, less well on those criteria. Just as the apocryphal AI was able to categorise the tank-pictures by whether the pic was taken in sunshine, the hiring-algorithm AI might categorise potential hires by whether they are white or male.

Even if you don’t tell the AI people’s sex or race, it may not help, because it may be able to work it out to a high degree of accuracy from proxies – postcodes, for instance, or first names.

The job-interview AI apparently looks at facial expressions, and listens to the words the interviewee uses, and compares it to 25,000 “pieces of facial and linguistic information” taken from people who have gone on to be good at a job. (Incidentally, the description that the company’s CTO gives of the analysis of verbal skills – “do you use passive or active words? Do you talk about ‘I’ or ‘we’?” – is deeply suspect from a linguistic point of view. The idea that people who say “I” a lot are narcissistic is a myth, and people talk an awful lot of crap about “the passive”.)

But, again, the AI can only be as good as the training data. It may be that “people who turned out to be good at their jobs” were more likely to have certain facial expressions or turns of phrase, and it may well be that those facial expressions or turns of phrase are more common among certain ethnic groups. And “people who turned out to be good at their jobs” are, of course, people who got hired in the first place. It is almost impossible to remove hidden biases. (I would be intrigued to know how, for instance, autistic people, or disabled people, would do on this facial-expressions stuff.)

That is not a reason to throw the whole idea of AI in hiring, or other areas, out. Algorithms – even simple ones – have been shown to do a better job of predicting outcomes than have humans in a wide variety of areas. The AI might be biased, but they are only biased because the humans they are replacing were biased too.

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For instance, Amazon’s famously sexist hiring algorithm that got scrapped last year only undervalued female applicants because the human hiring decisions had been systematically undervaluing female applicants themselves. It’s not that getting rid of the AI will get rid of the bias. In that case, it made it explicit. Amazon now knows that its hiring practices were biased. But it is a reason to be extremely wary of throwing AI into the mix and saying, well, now we’re unbiased, look, an algorithm did it, so it must be OK. And it’s doubly the case where the AI algorithm is proprietary — so you can’t look into its guts and see where it goes wrong.

That’s key. I said at the beginning that it is impossible to know how good the data you’re training an AI on actually is, and that’s true. But there are things you can do to try to find out. There are researchers working on something called “provably correct software systems”, which are somewhat misnamed – you can’t ever prove that software is correct – but you can go in and check parts of the data, or the weighting of the nodes in its network, which can increase your confidence.

If an AI is owned by a private company which won’t let you go and check the data or the algorithms, though (as is the case with the hiring one, and some of the parole ones), it becomes very hard to be confident. So for the time being, it’s worth being very, very wary of anyone who says their fancy AI can tell you who’s going to be good at their job. You can probably trust it to identify tanks, though.

That’s because any statistic that is interesting enough to have become a news story, he claims, has probably passed through “so many filters that encourage distortion and selection”, that “the very fact that I am hearing a claim based on statistics is reason to disbelieve it”.

I have a similar rule of thumb, which I’ve said here before: if a statistic is interesting, it’s probably not true.

The trouble is that we can’t do without them. There are simply too many people and too many things in the world to understand what’s going on without simplifying them and aggregating them into statistics; we can’t think about every individual NHS patient, we need to think in terms of average survival rates or waiting times. We have to treat people like numbers if we want to do the best for those people, so we need to learn to be better at interpreting those numbers.

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But statistics can be misleading — in some fascinating ways. In his book, Spiegelhalter, a Cambridge statistician and former president of the Royal Statistical Society, points some out; Simpson’s paradox, for instance. In 1996, the admissions data for five STEM subjects at Cambridge showed that men were more likely to be admitted: 24% of men’s applications were accepted, compared to 23% of women’s.

Is this evidence of bias? Well: possibly. But not, interestingly, of discrimination against women. In four of the five subjects – economics, engineering, computer science, medicine, and veterinary medicine – women were actually more likely to be accepted (in one, medicine, they were equal). Sometimes, that difference was quite stark – 16% of women compared to 12% of men were accepted onto the veterinary course; 32% of women against 26% of men into engineering.

How is this possible? Because medicine and veterinary medicine were much more popular, so your chances of being accepted were much lower; and because women were much more likely to apply for medical or veterinary degrees. By choosing the harder-to-access courses, women were less likely to be accepted overall, even though, individually, they were more likely to be accepted than a man applying for the same course. Similar findings have been seen elsewhere.

Another example is the “prosecutor’s fallacy”. Imagine a suspect is in court. If they’re innocent, there is only a one in a billion chance that their DNA would match the DNA found at the crime scene, and yet it does. Does this mean that there is only a one in a billion chance that they’re innocent? No; far from it. Nonetheless this has led to real women being wrongly convicted of child murder, after several of their children died of sudden infant death syndrome.

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An analogous problem is cancer testing. Say your new cancer test is 95% accurate; it will answer the question “do you have cancer” correctly 95% of the time. If you test positive, do you have a 95% chance of having cancer?

Not at all! Imagine that 1% of people have cancer. You run your test on 10,000 people. Of the 100 people who do have cancer, it rightly tells 95 of them that they do have cancer. Of the 9,900 people who don’t have cancer, it rightly tells 9,405 that they don’t have cancer. But this means that it has wrongly told 495 people that they do have cancer. If you test positive for cancer, your chances of having cancer are not 95%, but 95 in 590, or about 16%. The DNA-test situation is exactly the same: your test is “accurate” 999,999,999 times out of a billion, but will still regularly be wrong, because the thing it is looking for (the specific guilty person) is very rare.

These are just a couple of the reasons why even straightforward-sounding statistics can be misleading. They’re not even the big reasons – they’re just a couple that I thought you might not have heard of.

Things like statistical bias, small sample sizes, or multiple analyses of the same data will have more of an impact, as well as the Groucho Principle that the most dramatic findings are both more likely to be well-publicised and more likely to be false. But they’re illustrative of the fact that you need to be very careful of almost any statistical story you read.

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By Graeme Archer

At this point, I should admit that I have a dirty little secret, which is that I am not very good at maths. I mean, I’m fine. I’m OK. I got an A at GCSE, in 1997. But I’m not good. This is a problem, because I write an awful lot about statistics. Spiegelhalter tries to make the case that you don’t need to be very good at maths to be good at statistics, and he sort of succeeds – quite a lot of it you can grasp via the concepts, or through visualisations. But still, as an indifferent mathematician, I often found it hard to follow him.

As a non-mathematician, I have a few shortcuts for working out whether a statistic is worth believing, which seem to have done all right for me so far. One, which Spiegelhalter stresses, is that often the best statistical analysis you can do is simply visualising the data. There was a bit of a recent kerfuffle about suicides among girls and young women going up 83% since 2012; but simply looking at the ONS chart showed that the numbers were small, the data was noisy, and the only way you got the 83% figure was by choosing the lowest year on record. (It’s an old trick.)

Another useful hack is to ask “is this a big number?” Lots of statistics are presented as bald numbers: “X people have died”. But out of how many? If 25 people have died of cancer out of a population of 50, that’s awful; if 25 people have died of cancer out of a population of 50 million, that’s a statistical miracle. That’s one reason why I’m always wary of stories that say, for instance, “10 or more black transgender women have been killed this year” without telling me how many black transgender women there actually are, and whether that is more or less than we should expect.

Similarly, if a story tells you that something has “gone up by 25%”, or that something “increases your risk by 90%”, without telling you what it’s increased it from and to – if it gives you the “relative risk” but not the “absolute risk”, in technical terms – that’s an indicator that it may be worth ignoring. If something doubles your risk from one in 100 million to one in 50 million, you probably don’t care.

A third is asking “can I be sure what’s causing this?” Spiegelhalter dedicates a chapter to how you establish causality. It is amazingly hard to do this in the absence of large-scale randomised controlled trials. Austin Bradford Hill, one of the scientists – with Richard Doll – who first established that smoking causes lung cancer, suggested ways of doing so; looking for plausible mechanisms, making sure that the supposed effect follows the supposed cause, seeing if there’s a “dose response” – that is, the more of the cause there is, the more of the effect you see – and, importantly, making sure that the effect is sufficiently large that it couldn’t be a coincidence or some statistical confounding.

If you’re looking to see if something (say, a rise in teen suicides) is caused by something else (say, social media) then it’s worth looking to see if any rise in teen suicides follows the growth in social media, and whether each doubling in social media use leads to a consistent increase in suicide rate.

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But also, it’s important to see whether the suicide rate tends to jump around a lot anyway. If it does, then perhaps there’s nothing to explain; it just happened to be low one year and high another. (Spiegelhalter has a useful trick for seeing whether you’d expect to see the variation you see, such as using the mean averages to perform a “Poisson distribution”, but just looking at a graph and seeing whether it bounces around a lot is a good start.)

The statistician George Box said: “All models are wrong, but some are useful.” When you’re reducing millions of people and events to a few numbers, you’re building a model of the world, and that model must be simpler than the world, so it will be wrong in some way. You’re always making assumptions and drawing arbitrary lines.

For instance, we talk about the “murder rate” in Britain; statisticians treat it like an arbitrary event that happens at random to people, like decaying subatomic particles, with a 0.0008% or so chance of it happening to any given person per year. But that’s not how it works. Each murder is the result of real people making real decisions, not some lightning bolt out of a clear blue sky; it just happens that in a large enough population, non-random events can be treated as though they were random.

We can’t do it any other way. We can’t look at every person, and you can’t possibly predict every individual event, whether it’s the outcomes of heart operations or teen suicides or murders. You have to aggregate them, round off the corners, make simplifying assumptions; we have to treat people like numbers. You’ll get it wrong a lot of the time, and you have to be immensely cautious about any conclusions you draw.

There is one simple step, though, which will help a lot of the time. It’s a bit nihilistic, perhaps, but it’s powerful. And that is the Groucho principle that we discussed at the beginning: if the statistic makes a headline, it’s probably wrong.

What helps us do these things is efficiency: the one thing of which we can never have too much. But what if our constant striving to streamline is actually having a negative effect?

That’s the argument advanced by Edward Tenner in his new book, The Efficiency Paradox. Efficiency is “something self-evidently desirable,” he says, “until it isn’t”.

His beef is with big data (in which large collections of information are analysed computationally to reveal patterns, trends and associations), which is great at helping us to do more with less, or even to do less with much less. It’s not so good at helping us do something new and hitherto unimagined –those things that extend our capabilities.

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Things like flight. It wasn’t big data that invented the aeroplane, but risk-taking humans who took a chance on heavier-than-air flight being possible, in many cases throwing themselves from high places in pursuit of their dream. Once you have aeroplanes, efficiency – through the use of big data – can do wonderful things, such as minimise unwanted environmental effects, reduce prices to put flight within reach of ordinary people, and let us choose the seat we want online.

This is what Tenner, quoting Clayton Christensen, Derek van Bever and Bryan Mezue, calls “efficiency innovation”, typical of the platform-based disrupters such as Uber and Amazon, that “makes existing goods and services available to more people at lower prices”. But “market-creating innovation” that generates the new products or services, such as photocopiers or aeroplanes, takes longer to bear fruit.

Take the labour market. Optimists about the future of jobs in an increasingly automated age point to previous waves of mechanisation, when new industries arose to employ redundant workers. Cars created new jobs for people who previously worked with horses, for example, and then aviation created an entirely new industry that today employs about 10 million people worldwide, directly and indirectly.

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To some extent, I share that optimism; but for that pattern to persist in future, we need market-creating innovation to keep creating new industries. Those bigger gambles that won’t pay off for years, if ever, are less appealing to investors than the quick hits of market-disrupting efficiency innovation.

The most valuable companies in the US in 1917 made their money from steel, oil and the telegraph or telephone business. Today, the top five worldwide by market value are all in data, including Facebook and Google, whose business model rests on delivering advertising with ever-greater efficiency.

You could argue that those platforms are providing radically new products and services, albeit as a side product. Certainly, their existence has transformed the way we communicate with each other and find information. We learn more every week about how much data they collect about us – their users – and yet we continue to use the platforms we know are digitally surveilling and profiling us.

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This means that our experience of news and of our friends’ opinions, of videos created by users like us, or of political campaigns, is significantly mediated by algorithms that filter the endless tide of digital output. When those algorithms are gamed to feed us untruths, or horrific live videos of a suicide or murder, we ask why they can’t be made more efficient in preventing that kind of abuse.

The problem is, as Tenner puts it, that these “algorithms are written neither to advance a political outlook nor to establish the truth, but to maximise traffic and increase advertising revenue for the platform company”. The efficiency that the algorithms are designed to optimise is not for the benefit either of society or of the individual service user.

But “efficient at what, and for whose benefit?” is only the first question we should be asking. Tenner’s argument goes much deeper, challenging whether efficiency in itself is achievable or even desirable.

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Let’s go back to the “market-creating innovation” that produces new products and services. How can we make that innovation process itself more efficient? “The paradox of efficiency,” says Tenner, “is that progress towards greater efficiency is wasteful.” To make leaps of insight and creative imagination, human beings need to be inefficient. We need boredom, serendipity and “desirable difficulty”. We understand concepts better when we have to work harder to get them.

Experiments with reading on paper versus onscreen, with handwritten versus typed note-taking, and even with more and less legible typefaces, point to this “desirable difficulty” as a help in grasping underlying ideas. If you want to win a pub quiz, browse the internet on your smartphone. But to grasp big ideas, read a paper book in an archaic font, or listen to a lecture while making notes by hand.

Tenner has many other examples of how pursuing efficiency through technology and data can lead to its opposite. Using wearable tech to monitor physical activity and nudge us towards healthier lifestyles turns out, in experiments, to reduce activity, weight loss, and even happiness and well-being. Time and energy saved by home automation through the Internet of Things will probably be expended in the battle to secure networks against hackers.

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None of this means we should turn our backs on the algorithms – only that we should not rush to defer to their mechanical judgment. “Balancing algorithm and intuition to achieve inspired inefficiency,” as Tenner puts it, can free us from repetitive and time-consuming tasks to be creative and inventive. Combining contextual knowledge of a subject with the processing power of search engines or data analysis can deliver breakthroughs in discovery or invention.

The danger comes when we outsource our judgment, underestimating our human minds, shaped through the messy process of life in human society, and overestimating what algorithms can do. We may be fallible but, unlike computers, we understand what it means to live in this world, and we can imagine other worlds in which we might live in the future.

If we never go beyond doing the same things with increasing efficiency, we are missing opportunities that are no less important for being hard to quantify. Inefficiency is an unlikely rallying cry for the digital age, but it opens doors we might otherwise not even see.

Whoops! It was with the bland words “we apologise for the inconvenience” that the once mighty social network MySpace announced that in the course of a botched “server migration” they accidentally lost every bit of content uploaded to the site before 2016. That’s 50 million songs by 14 million artists (some of them, news reports solemnly warn us, including early work by the “MySpace Generation” of artists such as Lily Allen and Arctic Monkeys) as well as countless videos and photographs. All gone, gone utterly. Into the void.

Let’s say we accept that this really was simply a giant bit of incompetence rather than, as some tech people have speculated, a good way not to have to host 50 million unlistenable MP3s and toe-curling mid-1990s selfies in perpetuity. It’s disconcerting, eh? Didn’t we once believe that the internet was the place from which nothing ever disappeared? Didn’t we, indeed, once hope that?

Our default position as a species is to be in favour of memory. At the personal level we keep letters and diaries and photograph albums. At the state level we list buildings and erect monuments, fill museums and libraries and archives. We intone George Santayana’s dictum that those who cannot remember the past are condemned to repeat it. We recognise that scientific and medical knowledge are a dialogue with the past: we modify or build on it, or we overturn it. No Newton, no Einstein; no Einstein, no Hawking. The Dark Ages were so called (unfairly or not) because they were a period of forgetfulness. The notion floated in the 12^th century that we are dwarves standing on the shoulders of giants still resonates.

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Perhaps this is slightly heavy artillery to bring to bear on the disappearance into the digital never-never of, like, a gazillion bedroom jam sessions and early takes of a few Kate Nash B-sides. We’re not, you might think, in the same territory as the Taliban bashing up the Bamiyan Buddhas or Isis bulldozing Nimrud. We’re not looking at Venice sinking into the lagoon with a full cargo of Tintorettos. But there’s a principle here.

One of the basic enablers of human civilisation is what’s called “transactive memory”: that is, the outsourcing of personal memory to the collective, to something outside ourselves. That might be family members helping remind each other about what happened on holiday that year. It might be folk memory in the oral tradition. And later on it was the written word – and in due course the electronic ether. We share the cognitive burden and achieve more as a group than we can as individuals. Our tribal knowledge is held in common.

The problem with transactive memory, though, is that it tends to weaken individual recall – as has been recognised everywhere from Socrates’s suspicion of the written word to my consternation when Google Maps goes on the blink anywhere more than a three-minute walk from my front door. And we’ve been very blithe about the internet as the ultimate remembering machine. We tend to gloss over the way file formats become redundant, companies go bust and hardware fails. We tend not to think too hard about those old websites that now return 404 errors, or where the formatting is borked and there are red crosses or error messages where there used to be photographs.

And even to the extent that it does work there are two problems, I think, with the Funes the Memorious version of the Internet as a space where no human object is too trivial to be forgotten. One is the personal, practical one. Just as the law recognises the notion of spent convictions – that a crime committed and atoned for in youth need not follow the criminal for the rest of his or her days, and the DVLA will one day knock those points off my driving licence, there’s a rightful sense that people should not be personally and permanently accountable for everything they’ve ever said or thought or done. “Offence archaeology” – where unsavoury long-gone social media posts are spread virally in the hopes of “cancelling” the reputation of your enemies – is an object lesson in why.

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If every teenage indiscretion, every embarrassing photograph, every bad-taste joke, is ineradicable, one is the permanent captive of one’s younger self, as the US presidential hopeful Beto O’Rourke has recently discovered. The child really is father to the man: and not the sort of nice father who takes you to the pictures on Saturday and bounces you on his shoulders in the swimming pool, but the sort who locks you in the cellar and batters you for speaking out of turn. To err is human and to forgive divine: but in the absence of divinity forgetting will have to do. And, indeed, the “right to be forgotten” is now enshrined in Article 17 of the GDPR.

We might add that there are good commercial and moral reasons that big data should have a limit on how much information it can hold on us and for how long. Though we should also note the irony that we zoom between being outraged at the amount of information social media companies hold on us, and stricken at the idea that they might lose our precious data.

But the wider, non-privacy-related issue – the cultural rather than the personal issue – is the vast noise-to-signal ratio. Honouring the past doesn’t mean archiving everything. Museums select. Heritage organisations select: we list and protect only, in theory, the good stuff. Libraries, too, select – even copyright libraries are archiving only those books out of the millions written and submitted that have found favour with publishing houses. But as storage has become cheaper and cheaper, this principle has started to be forgotten. Since the invention of digital photography on mobile phones, for instance, my family photograph album grows by a few dozen photographs a week. Already its sheer size renders it all but useless.

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By Nigel Cameron

So much as we might deplore the loss of our MP3s into MySpace’s forgetting hole, it’s a helpful wake-up call. It asks us to stop assuming that everything we might want is automatically being backed up and will be available for us somewhere, and tells us that we can’t simply bung everything with complete confidence into an infinite digital attic. It asks us (both individually and at corporate level) to stop merely archiving and start curating: to select now what we want to keep and make sure that we have it in formats that we can be sure will survive.

And, at the deep level, it reminds us that the second law of thermodynamics is going to get us whether we like it or not. Everything that we rescue from time is temporary. Shakespeare’s verse may outlast that marble and those gilded tombs, but not – in the cosmic scheme of things – by all that long. All our pomp of yesterday is one with Nineveh and Tyre.

And one day, like the Anglo-Saxon poets who thought Roman ruins the work of giants, we too will gaze in melancholy wonder on the broken columns of the early digital age. “Wrætlic is þes wealstan, wyrde gebræcon;/ burgstede burston, brosnað enta geweorc.” (“This masonry is wondrous; fates broke it courtyard pavements were smashed; the work of giants is decaying.”) These words from the ninth century, I note without comment, have already outlasted the early works of the Arctic Monkeys. Ubi sunt.

I will soon arrive, with some relief, at the glorious destination of being completely uninteresting to men. It’s called old age.

I know about misogyny, then – all women do. I know how it summons wreckage in women’s lives. I am poorer because I am a woman, I have lower status because I am a woman, and I am less confident because I am a woman. Although I am brave in print, in life I squeak like Minnie Mouse, and I wish I didn’t. I have had sexual experiences I did not want because I am a woman: and still, I know I’m lucky. I am a rich, white, western woman. Some people call it privilege. I call it the thick end of the wedge.

But I did not know how extensive – how absolute – the web of misogyny is until I read Invisible Woman: Exposing Data Bias in a World Designed for Men by Caroline Criado Perez. I read it while shouting at my husband. Perez is a sensible, second-wave Feminist – not a fourth-wave imbecile who thinks that prostitution is a valuable human right to be defended by those noble activists previously known as pimps. She campaigned for a statue of Millicent Fawcett to be placed in Parliament Square and a picture of Jane Austen (a sanitised portrait in which Jane looks pretty, because you can’t have everything) to be placed on a bank note.

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By Julie Bindel

I read the book in a rage. What is sensed, and lived, by women in a patchwork across class and continent is presented, in Criado Perez’s plain, detailed and almost overwhelming prose, as a self-perpetuating and all-consuming system of oppression and denial, in which women are an appendage to men, who have designed the world entirely for their own comfort and safety. There is no anger in Criado Perez’s pages – she is too busy with evidence for that – just as there is, as far as I can tell, no malice in this vast system of misogyny. It was all done thoughtlessly, like a man running over a child in the dark. The truth is, they just didn’t see us.

The book opens with Simone de Beauvoir’s words: “Representation of the world, like the world itself, is the work of men; they describe it from their own point of view, which they confuse with the absolute truth.” Criado Perez then spends three hundred pages proving de Beauvoir right, in chapters on environment, workplace, design, health, and politics.

Almost everything is designed for men. Cities are designed for men. Transport infrastructure is designed for men: when, for instance, did a wise woman last take a baby on the London Underground, which is clearly, during rush hour, unsafe, and yet usually a woman’s work? Why is money tossed at road-building, which favours “the unencumbered man”, not provision of public transport, which more women use on their longer, more potted trails through life? Even parks are designed for men, for unless care is taken to design them for women’s safety, fewer women – and girls – will use them, and their health will suffer.

One fascinating anecdote tells how, when the toilets of a theatre were made gender neutral, it only increased men’s space, for they used the cubicles in both, while women were, due to their bodies, denied the urinals. Women spend 97 billion hours a year, she writes, looking for a safe place to relieve themselves, and this is likewise detrimental to health. Invisible Women is full of such maddening insights.

The generalisations are accurate – women earn less, have less, care more for children and the elderly (which is spun, deceitfully, as “choice”) and slip into invisibility as the system devalues them. The would-be powerful woman – Hillary Clinton, for instance – is struck down for her presumption, and replaced with a dangerous imbecile. But it is the detail that compels. For instance, in Britain, the Pregnant Workers Directive does not apply to politicians; there is, Criado Perez writes, “no provision for them [female politicians] to vote without turning up in person”. For politicians! In July 2018 the Conservative MP Brandon Lewis was paired with the pregnant, and absent, Liberal Democrat MP Jo Swinson. But he forgot, and voted anyway, and he got away with it. Apple, in 2017, declared that its US HQ was “the best office building in the world”. It had a spa and a dentist. It did not have a creche.

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To close the gender pay-gap we need a family-friendly work culture

By Peter Franklin

I would describe the condition of women in the workplace as a shit storm. People who believe they are meritocrats are the least likely to be meritocrats. Universities and tech companies who boast of being ‘woke’ are among the worst employers. Bias against women is everywhere – see the archetypes – and without care and research, it thrives. Blind auditions for orchestras raised female representation by 45%. In academia, double-blind reviews (where the author and reviewer are anonymous) mean that female authored papers are more likely to be accepted, and better rated. For women to thrive as men do, their gender must, quite literally, be masked.

This bias leaks into the schoolroom – where girls learn that scientists and geniuses are male; misogyny is not inbuilt, but learned. Algorithms, invented by men, favour men. We corrupt children, and we corrupt computers, and, in turn, they corrupt us.

Every victory contains a defeat. Female soldiers are injured off the battlefield because they are forced to march in step with men. Packs that took breasts into account were not invented until 2011, 35 years after women were admitted to US military academies. Female coastguards in the UK cannot easily use the toilet on duty – see the 97 billion hours above – because the clothes are not designed for them. In 1997, a female police officer died in the UK because she removed her body armour to operate a hydraulic ram, which she could not use while wearing body armour that did not fit her because it was designed for a man.

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The women we erased from history

By Patricia Fara

There is too much death in this book – some caused by sexual assault, because women’s spaces are unsafe or non-existent, and some caused by negligence, because women’s bodies are forgotten. There is so little investment in pelvic floor health, for instance – men decide what is suitable for research, and they forgot the female pelvic floor – that a current scandal details how women with prolapsed vaginas had mesh inserted as a cure. Many were left with chronic pain. At least one, in Scotland, has died. Women seeking help for physical problems are dismissed as fantasists, or trouble-makers. Viagra is plentiful. So is death in childbirth.

It’s comic sometimes, if you care to laugh at darkness. The voice command software in a Ford Focus did not respond to the female driver, but it did respond to her husband in the passenger seat – because it was programmed to hear a deeper voice. Criado Perez lost me only once, when she noted that the average smartphone is too small to fit easily into a woman’s hand. Not important, I thought, but then she detailed a riot in which a female researcher could not take a photograph with one hand, while a male one could. It’s a tiny thing, but it brought disadvantage, glibly.

This is a brilliant book, then, to thump the gropers and the deniers with – the people who point out masculine suffering, which is real, to detract from female suffering, which is likewise real. Everyone should read it.

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Can capitalism ever be good for women?

By Vicky Pryce

The Manchester-based project features in a fascinating article by Jocelyn Kaiser and Ann Gibbons for Science magazine. Basically, the UKB is a massive database containing the standardised, searchable medical details of half a million individuals. Crucially, it includes genetic information:

“From 2006 to 2010, the UKB enrolled 500,000 people aged 40 to 69 through the United Kingdom’s National Health Service… Investigators sampled their blood and urine, surveyed their habits, and examined them for more than 2400 different traits or phenotypes, including data on their social lives, cognitive state, lifestyle, and physical health.

“The blood samples yielded DNA for genomic analyses…”

This rich and extensive source of data allows researchers to uncover all sorts of correlations between different genes, medical conditions, behavioural traits, lifestyles and life outcomes. Though correlation does not necessarily imply causation, it does help identify promising avenues for further research.

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By Peter Franklin

It’s not just what’s in the database that makes it special, but also how easily researchers can get information out of it:

“The crucial ingredient, however, may be open access. Researchers around the world can freely delve into the UKB data and rapidly build on one another’s work, resulting in unexpected dividends in diverse fields, such as human evolution. In a crowdsourcing spirit rare in the hypercompetitive world of biomedical research, groups even post tools for using the data without first seeking credit by publishing in a journal.”

Are the UKB and its funders (which include the Wellcome Trust and the UK government) missing a trick? Why not commercialise such a valuable data source instead of giving it away?

That, however, is a very low dimensional way of thinking, one that ignores the benefits of sharing:

“‘The U.K. is getting all of the world’s best brains’ to study its citizens, says Ewan Birney, director of the EMBL European Bioinformatics Institute in Hinxton, U.K., and a member of the UKB’s steering committee.’”

It also burnishes the reputation of the UK as a world centre of excellence in bioscience – not to mention a place where the ultra-patient investments of the public voluntary sectors can happily coexist and cooperate with those of the private sector.

Not so long ago, open access, or ‘open source’, was the buzzword of the digital age and “information wants to be free” its motto. The success of operating systems like Linux and websites like Wikipedia seemed to provide all the proof needed that open source was the future.

But that was before the likes of Facebook and Google showed how ostensibly free services could be used to build trillion dollar businesses on the basis of relentless data extraction and commercialisation. Indeed, in controlling data flows, they have essentially privatised whole chunks of the internet, creating gated marketplaces in the process.

Thus as well as its direct contribution to scientific research, the UKB also serves as a vital reminder that profit-hungry data-hoarding is not the only way.

With the giants of Silicon Valley pushing into healthcare and technological progress increasing the speed and affordability of harvesting genetic data, can the UKB model hold out? In the era of ‘the quantified self’ will we surrender our medical data to the tech giants as easily as we’ve surrendered our social and consumer data?

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Is Amazon's attempt to disrupt the healthcare industry heroic or villainous?

By Peter Franklin

There are reasons to hope that Britain in particular can hold back the tide. As well as an impressive range of private, public and independent research institutions, there is our trump card: the National Health Service.

Like all healthcare systems, the NHS is riddled with various inefficiencies and dysfunctions. It should not be above criticism, but even the NHS-sceptics ought to recognise just how well-placed it is for the era of biomedical big data.

Firstly, it is a national health service – a single organisation, providing a broad spectrum of healthcare services to a large and diverse population. Though the data already in the UKB is immensely valuable, it represents a tiny fraction of the potential resource.

Secondly, the NHS is trusted and loved by the people it serves. That goodwill means that, as a nation, we’re willing to help as well as be helped – for instance, by giving blood. I doubt that millions of us would be willing to donate our erythrocytes to a commercial concern. It is the same goodwill that could persuade millions wore us to become data donors.

Thirdly, the NHS provides an answer to concerns about how our medical data might be used – and especially our genetic data. The nightmare is that if the genes of every individual were to be sequenced and recorded, a ‘genetic underclass’ would emerge – denied affordable medical insurance because of what’s in their DNA. The NHS, however, is free at the point of need to every citizen regardless of genetic inheritance.

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How Dr Internet got smarter than the real thing

By Peter Franklin

Long-term projects such as the UKB also have a reassuring record of anonymising and protecting its donors’ data. According to Kaiser and Gibbons, organisations accessing the UKB have to “sign a legal agreement not to try to reidentify any participant”.

This good practice needs the full backing of the state. British policy makers should be striving to establish the UK not only as the world leader in medical big data, but also in medical confidentiality. The two objectives may be in tension with one another, but they also need to be reconciled. The more that law-abiding citizens can be confident that their own genetic information will not, and cannot, be used against them, the more they will be ready to share it with scientists.

Ultimately, genetic research depends upon genetic privacy.

The early internet was a pretty innocent undertaking. Its architects were similarly-minded, collegial academics scattered across a range of elite universities and defence science laboratories. They knew that computers needed a common language, a common series of rules, if they were ever going to be able to talk to each other. There were a number of competing visions, but one protocol eventually won. It emerged to define the internet: called TCP/IP.

Transfer Control Protocol/ Internet Protocol. Even its name is unimaginably boring. Hardly anyone knows how the protocol works, and the vast majority of people who rely on it every day don’t know that it exists. But this tangle of code is undoubtedly more important than any law, any charter or covenant, for enshrining the rights, at least online, that you enjoy today. Just a series of rules, TCP/IP was written into the way the internet worked, and it reflected that innocent time.

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Who commands the internet?

By Carl Miller

The internet, TCP/IP says, must be free to use. It is a network that any computer, anywhere can join. All the data that passes through the internet, it says, must be treated equally, and computers are entirely disinterested about what that data is. And because of the way that it works, the internet would have no reigning authority or centre. No-one would be – really could be – in charge of it. It was a vision for a free-wheeling, open, democratic network of networks.

As the decades have passed, however, the internet has grown beyond the wildest, most unguarded imaginations of the people who first built it. From an innocent network of researchers, it is now more likely to be seen as a playground for hate criminals, information warfare officers and organised cybercriminals.

And yet TCP/IP has maintained its innocent outlook, still letting anyone join, still treating all data as equal still, essentially, trusting people to be who they say they are. Its architects might have imagined the internet as an escape from the tawdry geo-political struggles that blighted offline life, but it is now a key battleground where those very conflicts are being fought.

As the internet has fallen from its state of innocence, protocols – those powerful, forgotten, mysterious things – are now part of that struggle. There is now a competitor to TCP/IP, a very different kind of protocol, that has been rising in popularity. 

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Analogue dictators are no match for digital detectives

By Ian Birrell

It is called Digital Object Architecture, or, another acronym, DOA. Created in the 1990s, by one of the original inventors of the internet, Bob Kahn, DOA has another way of managing information. Unlike TCP/IP, data isn’t anonymous. Everything on the internet gets its own, unchanging digital fingerprint, whether a device, document or a person. Everything gets an identity number that doesn’t change.

DOA has been used to manage libraries and online academic repositories. But it is now being proposed to make the internet more secure. Forged identity is one of the main reasons crime is so easy to commit on the internet, and DOA would likely help change that. A hacker would struggle to impersonate you, because they wouldn’t have your unique identifier. Likewise, a fraudster would struggle to pretend to be your bank. Your financial records, your medical data, your social media profile – everything could be tagged to ensure that only certain people could look at it. It would weave identity and security into the fundamental way that the internet works.

For its backers, however, DOA may solve an even bigger problem than cybercrime. The internet, as it stands, has always been very difficult for states to control. In order to work, huge databases may have to be built, where every digital object is uniquely tagged, and where information is added, tracked and queried. Whomever controls the DOA registry becomes the centralised gatekeeper to all the information, resources and devices on the internet. It would create an unprecedented concentration of power to control and manage online life. And who would probably own the DOA registries? Governments. 

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By Carl Miller

There are reports – confirmed and unconfirmed – that DOA has begun to be rolled out. China may have been first in 2014. In May 2016, the Russian Minister of Communications Nikolay Nikiforov, said “Russia is interested in designing alternative ways of addressing on the internet, such as the DONA [an authority to implement DOA]”. On 16 October 2016 South Africa signed an agreement to use DOA. It’s not clear who else is using it, and that, of course, is part of the problem.

Human rights always exist in tension with one another, and these protocols that run the internet are at the very centre of one of the most important of these tensions. It’s a lot harder for any State to control an internet governed by TCP/IP, but it’s also easier for criminals to flourish. DOA would likely make cybercrime more difficult, but the price would be that governments would have a level of control over digital life that at the moment they simply do not have.

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The man holding Putin to account

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This is a big deal. Different governments will take different views on the right protocols for the future. Western governments will probably give DOA a wide berth, whilst others have already adopted it. If that’s the case, we might be heading towards the ‘splinternet’ – a fracturing in the basic way that the internet works across geo-political divides. Some countries will use one protocol, and others another.

But underlying any kind of geo-political fracture in the internet is, for me, a more fundamental concern. Historically, human rights need to be protected by more than governments. They need high public visibility and the aggressive, vocal advocacy of civic society.

Internet protocols may become one of the most important battlegrounds where we fight for our online rights, freedoms and responsibilities. But surrounded by an alphabet soup of international agencies, legalese and impenetrable technical verbiage, they are difficult to explain, to understand, to raise any kind of awareness about. For all but a small handful, they are simply too impenetrable and boring to feel angry about. And that is a huge, huge danger.

I’d always assumed that ‘Lithium’ was a brand name for a psychiatric drug – like ‘Valium’ – and nothing to do with the third element in the periodic table.

I was, of course, wrong about that – lithium in the form of lithium carbonate (or some other lithium salt) is used to treat mental conditions, especially serious depression.

My incorrect assumption was based on another incorrect assumption, i.e. that any substance with a significant pharmaceutical effect would need to be a complex organic compound – either extracted from a plant or synthesised in a laboratory. That a simple mineral salt can be used as a powerful drug was news to me.

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By Peter Franklin

Here’s a further twist: lithium salts are naturally occurring, which means that some areas have small but significant amounts of it in the water supply.

Writing for Vox, Dylan Matthews and Byrd Pinkerton explain that this could be having a measurable impact on mental health:

“Are places with more lithium in the water healthier, mentally? Do places with more lithium have less depression or bipolar or — most importantly of all — fewer suicides?

“A 2014 review of studies concluded that the answer was yes: Four of five studies reviewed found that places with higher levels of trace lithium had lower suicide rates. And Nassir Ghaemi, the Tufts psychiatry professor who co-authored that review, argues that the effects are large. High-lithium areas, he says, have suicide rates 50 to 60 percent lower than those of low-lithium areas.”

Correlation is not causation, of course – and using statistics to eliminate other potential factors its a tricky business. Furthermore, not every study confirms the link between naturally lithiated water and reduced suicide risk.

Nevertheless, there are those who believe the evidence is strong enough to justify artificially boosting lithium levels in areas where concentrations are very low:

“Ghaemi and a number of other eminent psychiatrists are making a pretty remarkable claim. They think we could save tens of thousands of lives a year with a very simple, low-cost intervention: putting small amounts of lithium, amounts likely too small to have significant side effects, into our drinking water, the way we put fluoride in to protect our teeth.”

One can only imagine what the backlash to such a policy would be. The conspiracy theorists would have a field day. Putting flouride in water was controversial enough – and that’s only intended to prevent tooth delay. A chemical literally intended to influence our minds would be another matter entirely.

And, yet, if it could save tens of thousands of lives a year it would seem heartless to object. In fact, if it was ‘just’ thousands, or even hundreds, there’d still be a powerful case (assuming the evidence stacked up).

But where do we stop? In an age when big data generating technology allows us to monitor our health on a continuous basis (the ‘quantified self‘) while gathering vast quantities of information about the day-to-day world we live in (environmental monitoring) – we could establish a statistical case for adding hundreds of different substances to the water supply. Must we all be compulsorily medicated to achieve the desired effects?

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Letting the gene genie out of the bottle

By Peter Franklin

And what if our big data methods, combined with a growing understanding of the influence of our genes on  health outcomes, show that an intervention that benefits one particular group of people is actively harmful to another? How would government decide between these conflicting interest?

As, I’ve written before, the biggest lesson we need to learn from the new genetics (and from medical advances in general) is that government interventions need to be personalised. Drugging the water supply would seem to represent exactly the opposite approach.

Today, the $740 billion former bookseller’s tentacles stretch far beyond e-commerce: into bricks-and-mortar retailing, air cargo, cloud computing and AI – technologies that are, or will become, the backbone of countries such as the USA and the UK. It has combined traditional market dominating techniques with an unrivalled understanding of its customers’ online behaviour, to awesome effect. And concern about monopolistic behaviour is increasingly being voiced.

Further reading

Can't we stop data monsters from devouring their rivals?

By David Morris

Everyone used to understand what a monopoly was, and nearly everyone, other than monopolists themselves, understood that it represented the failure of capitalism rather than its apex. When one company dominates a market then the good things that come from competition between producers, such as quality, variety and innovation, are likely to come to an end. Without competition, there’s little incentive to do any of these expensive things, and both consumers and the wider economy loses out.

Then in the 1970s a new idea emerged from America’s law schools that turned this thinking on its head. The size of the company no longer mattered: the only criterion by which it should be judged, or a takeover blocked, was consumer welfare. To put it crudely, a firm like Amazon could be as big as it wanted as long as it kept prices low. Today to be concerned about the ‘bigness’ of companies is to go back to the popularist goals of the past, argues Professor Daniel Crane of the University of Michigan.

By the second decade of the 21st century, that reinterpretation of the law has had – if you are cynical – its intended effect. We are living in a new age of concentration, where a few corporate giants like Amazon dominate the economy. The ratio between the cost of a good or service and its selling price in the wider economy – often seen as evidence of market dominance – has risen in advanced economies by 43% since the 1980s.  The combined revenue of the tech giants – Alphabet, Amazon, Apple, Facebook and Microsoft – is larger than the GDP of more than 90% of the world’s nations. Start-ups are even founded with the sole purpose of being acquired by one of these Big Five.

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Who commands the internet?

By Carl Miller

Then, in 2016, an unknown law student decided to dust down some forgotten textbooks hidden away on the top floor of her university library. Early in 2017, Lina Khan published ‘Amazon’s Antitrust Paradox’ in the Yale Law Journal. The one word that Big Tech didn’t want to see in the public conversation – monopoly – was back, and at the worst moment. For the first time, Big Tech was caught in a bipartisan squeeze in Congress as real political pressure built up over the abuse of customer data and fake news.

Khan’s argument was simple. Once laws that were designed to prevent one company controlling a market had been pushed to one side, companies like Amazon were able to gain market dominance through anticompetitive practices. This gave them control of the infrastructure that many businesses and other parts of the economy depend on. Khan suggests that either Amazon should be regulated more forcefully to ensure competition – or that it should be recognised as a natural monopoly like the water or gas supply, and run as a publicity utility.

Now, Amazon, Google and the other modern monopolies have a battle on their hands. Worryingly, it looks like they have a formidable range of weapons to fight it with – and Khan and her allies have already been labelled as “hipster radicals.”

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How the big tech companies are eating free markets

By Charlotte Pickles

Big Tech’s lobbying dates back 20 years, but the scale of it has grown significantly. In America, five years ago Amazon spent $3.5 million on lobbying, last year they spent $13 million. In just a few years, Amazon’s lobbying operations have gone from almost nothing to rival Google. To defend its sprawling empire from the politicians that are circling the tech giant has assembled the biggest lobbying team of any tech company in Washington: 28 in-house lobbyists, compared to Apple’s and Facebook’s eight. 

Perhaps more disturbing is the influence that companies like Amazon and Google have that you can’t see. Both regulatory capture, where the regulators identify too closely with the companies they are trying to control, and policy capture, when commercial interests so infiltrate governments that they can affect the design of measures relevant to their activities, should concern us.

Since 2005, concern has grown over Google’s political influence after research showed that it had poached at least 65 government officials from all over the European Union. During the same period, 15 ‘Googlers’ have been appointed to government positions in Europe, thus penetrating to the heart of the decision-making process. In the UK, Google has targeted for recruitment civil servants from key parts of the government machine like the Treasury and No. 10. This revolving door between politics, tech and regulators is a key characteristic of crony capitalism.

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By Mark Piesing

Then, of course, there is the ‘dark money’ – the think tanks, research institutions and publications who enjoy their journey on Big Tech’s gravy train and can be used to shape the debate. Last year a critic of Google was fired from the New America Foundation, when he posted an article in support of the European Commission’s antitrust regulators. The think tank has received over $20 million from Eric Schmidt, co-founder of Google, his family trust and the company itself.

Then in July 2018, US activists really woke up to the power of Amazon, and the damage it could do to democracy, when the company used its influence to repeal a law passed by its home city of Seattle that imposed a small employee ‘head tax’ on large corporations. The purpose of the tax was to fund affordable housing in a city whose horrendously high costs are partly down to the very same tech companies. Many were left with the feeling that Amazon was too powerful to tax and regulate.

If politicians can be persuaded to act, the big question is how to tackle the problem. The traditional anti-monopoly medicine would be to break up these large conglomerates into their smaller component pieces. Amazon could easily be split into retail, cloud, logistics and video production. Alphabet (Google’s parent company) could be broken up into divisions such as Google Search and YouTube.

Further reading

How to bring the tech giants to heel

By Mat Lawrence

Others are thinking about an updating of the vision behind the BBC. In Britain, the think tank IPPR argues that the digital economy should be reorganised as a “digital commonwealth”, to be an open, collective resource for the common good. The major digital platform companies would be regulated like broadcasters and mobile providers. Where large digital companies have the advantage of existing data when moving into a new market, such as banking or mobility, they would be compelled to open it up for others to use. And a new public service corporation would ensure that public data is widely available for innovation and public good.

This is already being done in other industries. In the energy industry, consumers who share their data with new firms such as Switchd can already get better access to cheaper prices, and the process will only get better, faster and more automated as energy suppliers share more of their data digitally. Banks and other financial institutions are also opening up their data for others to access, use and share. Customers are doing something similar by sharing data from their accounts with third parties they trust.

In the end, the convenience of Amazon’s platform has turned us into lazy consumers who would give up almost anything in return for same day delivery. But if we are not careful it won’t just be the High Street that we have abandoned. Big Tech has the will and the wallet to bypass traditional regulators, and that leaves few options other than to turn them into social enterprises that serve the common good.

In 1911, the breakup of John D Rockefeller’s monopolistic Standard Oil Company prevented one man from having a stranglehold on the fuel that America depended on. If data is the new oil, then perhaps it’s time we stopped a handful of men from controlling the lifeblood of every advanced nation.

Further reading

Time to topple the technopoly

By Charlotte Pickles

In my own industry of newspapers, titles have seen advertising, sales and budgets crash. Most now fear for their survival, leaving journalists looking anxiously into the future and plotting potential escape routes. I spent 12 enjoyable years as deputy editor of The Independent, which was born amid great excitement the year after I began on a local paper yet has since disappeared as a physical product. A new City University analysis shows the impact of its retreat online – readership and global reach have increased, but time spent reading its content has plunged dramatically.

This is depressing. As a foreign reporter, I am fortunate to write for a title that still invests in journalism abroad. I hear often from colleagues on other papers, though, how budgets drastically restrict their work, especially when it involves travel. Yet as an optimist, I also see much to cheer – and not just from increased accountability in this unruly digital age. As one prominent investigative reporter reminded me this week, it is far easier to probe financial misdeeds when so much information is accessible from a computer rather than having to spend days trekking around libraries, offices and town halls to comb through files and official records.

Further reading

How digital dissidents are defying the despots

By Ian Birrell

Two recent exposes have given us further examples of how online data, combined with crowd-sourcing and cross-border collusion, is empowering journalism. In both cases, teams used readily-available information to demolish the lies of murderous regimes. Analogue dictators, even backed by the full power of states, are no match for smart investigators in the digital age. And they show how journalism is adapting to this turbulent era by finding transformative ways to break significant stories of global interest.

The first report was by BBC Africa into chilling atrocities captured on video that went viral on social media in July. The footage is horrific, showing soldiers leading away two women and two young children before shooting them. This is murder in the coldest of blood. It was thought to have taken place in Cameroon, although the government dismissed it as ‘fake news’ and some said it was from Mali. So Africa Eye began to investigate with journalists and freelance digital experts, using methods trialled on footage of soldiers burning villages in Cameroon’s Anglophone region.

They matched geographical details with satellite images to pin down the location after receiving a tip, then assessed shadows cast by the killers to tie down the date with a specialist app. They examined weapons and uniforms to prove the soldiers were from Cameroon, despite government denials, by matching them with pictures on Facebook. Social media even helped identify one man murdering a little girl. “Astonishing investigative journalism, which uses digital technology to amazing effect, cannot be championed or shared enough,” said Amol Rajan, the BBC media editor (and final editor of The Independent newspaper). He is right – and so far more than 55,000 people have shared BBC Africa’s thread on Twitter alone.

This astonishing investigative journalism, which uses digital technology to amazing effect, cannot be championed or shared enough. It’s a rigorous, brave, campaigning fact-check – but told in a riveting narrative. And beautifully written. Deserves every prize coming. https://t.co/2ROV8c7SZJ

— Amol Rajan (@amolrajan) September 25, 2018

This technique adopted open-source investigative methods pioneered by Eliot Higgins, the 39-year-old founder of Bellingcat from Leicester. He started out when, bored in his administrative office job, he saw evidence on social media of ethnic cleansing in Libya that was being missed by journalists covering the 2011 war. “I saw all this information and thought I would begin writing about it,” he told me. “I had no idea if anyone would be interested.”

Originally blogging as Moses Brown, he helped create a form of citizen journalism collating crumbs of information from online sources to discover the truth about contested events. The spread of smart phones meant a sudden surge in information available online. Bellingcat’s work proved Russian links to the shooting down of Malaysian Airlines Flight MH17 – especially impressive having seen the shattered bodies strewn around wild flowers in fields as Vladimir Putin’s stooges strutted about and looted the corpses. Their work proving Bashar Assad’s use of chemical weapons in Syria was also stunning.

Now Bellingcat – with a team of 10 full-timers and 15 volunteers, backed by grants and crowdsourced funds – is assisting the International Criminal Court, while Higgins delivers workshops sharing his techniques with activists and journalists. He shows how to use satellite images to identify burned out towns and explains how to handle Middle East sources without speaking Arabic. And this week, in partnership with a Russian investigative site, we saw again the impact of his group’s painstaking methods with another bombshell expose that ripped apart the latest Putin lies.

Further reading

The threat of ‘fake news’ isn’t fake, but nor is it new

By Peter Franklin

In a superb fusion of old-fashioned journalism – relying on tips, sources and hunches – combined with sophisticated online search techniques, the investigators showed how one of the suspects in the Salisbury poisoning incident was not a civilian on a harmless tourist trip but a highly-decorated Russian officer. It made the Kremlin and its Russia Today propaganda station look ridiculous. Much of the information was sitting out there in the open, even in Russia. It just needed skilled people to harvest it, analyse it and share it with the world

It is another scoop of global importance, followed up on the front pages of British newspapers. It showed the strength of a collaborative form of investigative journalism that is now being taken up by traditional media from the BBC through to The Guardian.

As often with digital disruption, the big step forward came from an outsider. Amid all the fuss over fake news, the swirling mist of conspiracy theories and savagery of so much online debate, this serves as a reminder that we can cling on to some of that early optimism about the internet as a force for good. Such reporting shows technology offering hope as well as fear for those in my trade – and for those seeking truth in these tempestuous times.

Further reading

In defence of the Fourth Estate

By Ian Birrell

The answer, is that we make prior assumptions about what (and who) is trustworthy and relevant. Furthermore, we tend to do so collectively – subscribing to the shared assumptions of the groups we want to be part of.

Fair enough. We need those filters, otherwise we’d be overwhelmed. The problem though is when the filter become so restrictive that it only allows through the information and ideas that confirm existing assumptions. From that point, it ceases to be a defence and becomes a cage – a bubble within which different perspectives have no place and new understanding becomes impossible.

Further reading

Don’t believe what those idiots are telling you about echo chambers

By Peter Franklin

Such bubbles are typically constructed from linguistic components – facts, theories, jargon, slogans, arguments, talking points, memes, cultural references. However, the thickest bubbles are made out of numbers not words.

While words are open to interpretation, numbers are an expression of objective truth: 2 plus 2 equals 4 and nothing else. We may fear the dark arts of statistical manipulation, but what could be more objective than a direct, unfaked observation?

Unfortunately, there are pitfalls there too. The point is made in an eye-opening piece by Aaron E Carroll for the New York Times. It concerns that most wholesome of corporate activities – the workplace ‘wellness’ programme.

By now, we all know that sedentary lifestyles aren’t good for us – and especially not in combination with mental stress. This, in turn, is bad for productivity, employee retention and the cost of providing health insurance. Hence the rationale for workplace-based schemes to promote healthier lifestyles.

But do they work?

“These… can offer screening for a variety of reversible conditions; access to weight-loss programs or gyms; encouragement and support; and sometimes even chronic disease management. Many of the analyses of these programs have shown positive results.

“Almost all of those analyses are observational, though. They look at programs in a company and compare people who participate with those who don’t”

Surely, that’s a perfectly fair test – a direct, unmanipulated measurement of the difference that participation makes.

There’s a catch though:

“The most common concern with such studies is that those who participate are different from those who don’t in ways unrelated to the program itself. Maybe those people participating were already healthier. Maybe they were richer, or didn’t drink too much, or were younger. All of these things could bias the study in some way.”

Of course, one can control for factors like age, sex, income etc. This requires a statistical trick or two, but as long as the effect is to compare like-with-like (e.g. slightly overweight middle-aged men with other slightly overweight middle-aged men) who could object?

Further reading

If you don't 'get' statistics it's just as bad as not 'getting' Shakespeare

By Graeme Archer

Only, there’s another catch:

“…we can never be sure that there aren’t unmeasured factors, known as confounders, that are changing the results.”

When it comes to getting fit, some people are just more motivated than others – just the sort of people you’d expect to volunteer for a wellness programme. Motivation, being an internal state of mind, is hard to measure objectively and thus control for – meaning that the participating group is likely to be biased to those most inclined to make the most of it.

Then there’s bias on the part of the organisers. If it’s your job to make a company wellness programme a great success, who are you going to encourage to take part – the health-conscious individual looking to get back in shape or the unrepentant, donut-scoffing flubba-wubba sat at the next desk?

There is a solution though: the randomised controlled trial – where participation is randomly assigned.

Carroll describes one such trial – of a wellness programme among university employees. This found no significant difference in outcomes between the intervention group and the control group. But here’s the twist:

“…the researchers also took the time to analyze the data as if it were an observational trial. In other words, they took the 3,300 who were offered the wellness program, then analyzed them the way a typical observational trial would, comparing those who participated with those who didn’t.”

When the trial was ‘de-randomised’ – i.e. based on simple observation of participants versus non-participants the hoped for differences in outcomes did appear. Furthermore, most of those differences remained even when factors like age and prior healthiness were controlled for.

Clearly, selection bias is a powerful thing.

Further reading

Wanted: a government willing to perform experiments on us

By Peter Franklin

There are some big implications here for public policy.

The idea that public resources should be allocated on the basis of evidence of effectiveness is a perfectly sound one. Furthermore, with the advent of ‘big data‘, the state is acquiring the ability to observe the outcomes of government intervention in greater detail than ever before.

However, the danger is that with so much data to play with, policy-makers will overlook the confounding factors of selection bias and other epistemic pitfalls.

Having left behind the dark age of pure guesswork, policy-making is set to enter a new age of false confidence.

The desire line is a natural form of crowd-sourced design and probably where the planners should have put the path in the first place.

Another example is something called a ‘Sneckdown’ – which is explained in a great post for the 99% Invisible site:

“Urban planners can learn a lot simply by observing where cars actually drive (or don’t) after a fresh snowfall. Author and activist Jon Geeting has been photographing ‘sneckdowns’ (a portmanteau of ‘snowy’ and ‘neckdowns’ or: curb extensions) in Philadelphia for years, highlighting areas that could be converted from vehicular to pedestrian use. Remarkably, his documentation has done far more than just create general interest — it has actually helped to reshape intersections.”

The sneckdown is the area of road which remains snowy because there are no cars driving over it. To put it another way, the area where traffic crushes the snow out of existence is the desire line – the portion of the road that drivers actually use.

In Philadelphia, campaigners were able to use the sneckdown photographs as evidence that pedestrian areas could be extended without impeding the flow of traffic – an act of urban reclamation with many benefits:

“Shortening pedestrian crossing time… Calming vehicle traffic around intersections… Reducing asphalt surfaces and increasing plant surfaces to improve the absorption of rainwater by the soil, minimizing runoff and floods.”

This is all rather wonderful, even ingenious, but does it have any wider relevance?

Yes, because it serves as a parable for the age of big data.

It won’t be long before we have a continuous stream of live information about our everyday interactions with the built environment and, indeed one another. Month by month, year by year, we’re adding to the automated array of cameras, microphones and other types of sensor that record what happens in the world about us – and our behaviour within it.

Recommended reading

Kiss your privacy goodbye – CCTV is just the beginning

By Peter Franklin

With these technologies in place it isn’t necessary to wait for a snowfall to work out the desire lines followed by pedestrians and drivers – it’ll all be there in a detailed log of our every movement.

However, as well as ensuring that this big data is anonymised, it needs to be made publicly available. While the paths we make in the snow are there for all to see and photograph, the electronic traces we leave behind could be hidden away for the exclusive use of state agencies or private corporations. If the big data that they collect is about us and the public spaces we share, then it is essential that we’re able to challenge and verify the conclusions that they draw from it.

In the not too distant future big data, plus the AI systems used to analyse it, will enable us to monitor and modify the public sphere in real time. To a growing extent it is already happening.

The strength of our civil society – indeed our basic liberties – will depend on keeping ordinary citizens in the loop.

“Perhaps the most popular current success formula is the 10,000-hour rule popularized by Malcolm Gladwell. The idea is that you need 10,000 hours of deliberate practice to become a world-class performer in any field.

“Research now tells us, however, that this formula is woefully inadequate to explain success, especially in the professional realm. A 2014 review of 88 previous studies found that ‘deliberate practice explained 26% of the variance in performance for games, 21% for music, 18% for sports, 4% for education, and less than 1% for professions. We conclude that deliberate practice is important, but not as important as has been argued.’”

Practice can give you an edge in some fields, but not others:

“…deliberate practice may help you in fields that change slowly or not at all, such as music and sports. It helps you succeed when the future looks like the past, but it’s next to useless in areas that change rapidly, such as technology and business.”

Sports and music are rules-based activities, but in technology and business the game is changing all the time. Therefore what is needed is not so much “deliberate practice” as “deliberate experimentation” – continually trying out new ideas and strategies to stay ahead of the game. 

If that’s true of business, then it’s surely also true of government. Even in the most free market of developed nations, the state directly controls a massive chunk of the economy. Public sector innovation is therefore as important as private sector innovation to our prosperity and wellbeing.

The state is well-practiced at what it does, but a deliberately experimental approach is rather less in evidence. Indeed the culture of government often works against innovation.

The secrets of successful innovation need to be embraced by the state, but what are they? Michael Simmons reviews the evidence:

“…most innovative ideas are generated by a small number of superstars. In any given field, the top 10% of performers produce more than 50% of breakthroughs.”

In an over-centralised state there are no superstars because the bureaucracy is monolithic. To allow the best to emerge from the rest, the state must be decentralised – with different bodies within government (especially the locally elected ones) empowered to experiment with different policies. If that produces different (and therefore unequal) outcomes that should be seen a feature and not a bug of the system. 

The system must also become more tolerant of failure, which is the tribute that experimentation pays to success:

“…superstars produce just as many bad ideas as everyone else — they just produce more ideas overall. Having many more ideas means they have more failures but also more hits.”

Policy makers must also beware of false productivity:

“…we live in a culture that is obsessed with productivity: getting more done and less time, systematizing, automating, and even outsourcing. If one has a frame of short-term productivity, then taking time out of your day to nurture a creative process with unpredictable results that don’t pay off immediately is extremely hard. What is productive in the long-term often feels not productive in the short-term.”

This is so important. Government is full of competent, cautious and profoundly unimaginative individuals. They are not, by nature, risk takers – and yet taking risks is what innovation (and enabling innovators) is all about:

“Given a ten percent chance of a 100 times payoff, you should take that bet every time. But you’re still going to be wrong nine times out of ten.”

That said, bureaucracy does have a part to play – in setting up the information gathering systems that allow the results of innovation to be systematically assessed. As Simmons points out “today’s tools allow anyone to increase their quantity of experimentation by an order of magnitude.”

For all of the data gathering power of a Facebook or a Google, nothing compares to the direct line that a modern state has (or should have) to vitally important activities like education and healthcare. 

An entrepreneurial, innovative state shouldn’t just be striving to find new ways of improving our lives, but also using the knowledge it gains to generate revenues for the public purse. A government that isn’t experimenting on its own people is letting them down.