net.wars

Review: Cloudmoney

Cloudmoney: Cash, Cards, Crypto, and the War for our Wallets
By Brett Scott
Publisher: Bodley Head
ISBN: 978-1-847-92587-9

Three years ago, the area around the local tube station included a bank and four ATMs. Come the pandemic, the bank closed, never to return, and so did two of the ATMs. The loss of the bank gave a couple of the chain stores an excuse to refuse to take cash. But they’re a minority in an area full of independent local shops, who recognize that many of their customers are cash users. Journey into some parts of central London, however, and cash gets you ghosted.

We are told that the cashless future is what we want: it’s more convenient (except when the system is down, the app needs to be rebooted, or there’s no Internet connection). The reality, as “monetary anthropologist” and former broker Brett Scott points out in his book Cloudmoney, is that despite this inevitability narrative, one reason electronic/digital payments are more convenient is a deliberate effort to make cash harder to access. Often, promoters claim the cashless society is – or will be – more financially inclusive. Yet, as Scott recounts, that “inclusion” in the remote global economy often brings with it the exclusion of locally-controlled, less formal economies. Less financial inclusion, more *enclosure* and “corporate seep”.

Scott’s central thesis is simple: once the forces of Big Tech and Big Finance have merged, they will have a hitherto unimaginable amount of power over all of us. I have some sympathy with this argument. People forget that it was through the banks that Gilead was brought into being in Margaret Atwood’s The Handmaid’s Tale. All they had to do was locate all the accounts tagged “F” and turn off access until a suitable male came forward to claim them. This is the power of cloudmoney – money that exists for us only in the form of numbers that represent promises to pay. Scott is not predicting a specific dystopia; but he does want to propagate a counterbalancing narrative to the “liberation” every new fintech app pretends to promise while scarfing up all our personal data. In his campaign to protect the public system of cash, he sometimes finds himself in the company of conspiracy theorists whose other ideas he rejects.

What is less clear is where bitcoin and other cryptocurrencies fit in. They also started with rhetoric: they were digital cash, digital gold, a mechanism for bypassing the world’s banks and governments. In practice, so far, they haven’t succeeded at any of these things, and even in El Salvador, where bitcoin is legal tender, you can’t use it to buy a box of oatmeal in a supermarket.

The story technology companies tell is, of course, that they are disrupting the stodgy, antiquated world of traditional finance. Instead, what Scott sees is plain old automation that serves that world and tightens its control. Almost every new service, whatever the rhetoric it starts with, from credit cards to Paypal to Apple Pay to Facebook’s failed Libra cryptocurrency, becomes a front end for bank accounts for the same reason that robbers always focused on them: that’s where the money is. The exception is cash – slow, partially disconnected cash that enables transactions that aren’t caught in what Scott calls the “digital mesh” of corporate capitalism. No wonder they hate it.

Excluding the vote

“You have to register at home, where your parents live,” said the clerk at the Board of Elections office.

I was 18, and registering to vote for the first time. It was 1972.

“I don’t live there,” I said. “I live here.” “Here” was Ithaca, NY, a town that, I learned later, was hyper-conscious that college students – Cornell, Ithaca College – outnumbered local residents. They didn’t want us interlopers overwhelming their preferences.

We had a couple more back-and-forths like this, and then she picked up the phone and called the state authorities in Albany for an official ruling. I knew – or thought I knew – that the law was on my side.

It was. I registered. I voted.

In about a month, the UK will hold local elections. For the first time, anyone presenting themselves to vote at the polls will be required to show an ID card with a photograph. This is a policy purely imported from American Republicans, and it has no basis in necessity. The Electoral Commission, in recommending its introduction, admitted that the issue was public perception. The big issues with respect to elections are around dark money and the processes by which candidates are chosen.

For 49 days in the fall of 2022, Liz Truss served as prime minister; she was chosen by 81,326 Tory party members. Out of the country’s roughly 68 million people, only 141,725 (out of an estimated 172,000 party members) voted in that contest because, since the Conservatives had decisively won the 2019 election, they were just electing a new leader. Rishi Sunak was voted in by 202 MPs.

The government’s proximate excuse for bringing in voter ID is the fraud-riddled May 2014 mayoral election in the London borough of Tower Hamlets. Four local residents risked their own money to challenge the outcome, and in 2015 won an Election Court ruling voiding the election and barring the cheating winner from standing for public office for five years. Their complaints; included vote-rigging, false statements made by the winning candidates about his rival, bribery, and religious influence.

The High Court of Justice’s judgment in the case says: “…in practice, where electoral malpractice is established, particularly in the field of vote-rigging, it is very rare indeed to find members of the general public engaging in DIY vote-rigging on behalf of a candidate. Generally speaking, if there is widespread personation or false registration or misuse of postal votes, it will have been organised by the candidate or by someone who is, in law, his agent.”

Surely a more logical response to the Tower Hamlets case would be to make it easier – or at least quicker – for individuals to challenge election results and examine ways to ensure better behavior by *candidates*, not voters.

The judgment also notes that personation – assuming someone else’s identity in order to vote – was far more of a risk when fewer people qualified to vote. There followed a long period when it was too labor-intensive for too little reward; you need a lot of impersonators to change the result. In recent years, however, postal voting has made it viable again; in two wards of a 2008 Birmingham election Labour candidates committed 15 types of fraud involving postal ballots. The election in those two wards was re-run.

In his book Security Engineering, Cambridge professor Ross Anderson notes that the likelihood that expanded use of postal ballots would open the way for vote-buying an intimidation was predicted even as first Margaret Thatcher and then Tony Blair pursued the policy. But the main point is clear: the big problem is postal ballots, which you can’t solve by requiring voter ID from those who vote in person. It’s the wrong threat model. As Anderson observes, “…it’s typically the incumbent who tweaks the laws, buys the voting machines, and creates as many advantages for their own side, small and large, as the local political culture will tolerate.”

But voter ID is the policy that Boris Johnson used his 80-seat majority to push through in the form of the Elections Act (2022), which also weakens the independence of the Electoral Commission. As the bill went through Parliament, estimates were that about 3.5 million people lacked any qualifying form of ID, and that those 3.5 million skew heavily toward people who are not expected to vote Conservative.

This was all maddening enough – and then they published the list of acceptable forms of ID. Tl;dr: the list blatantly skews in favor of older and richer people, who are presumed to be more likely to vote Conservative. Passports, driving licenses, and travel passes 60+ for people are all acceptable. Student ID cards and travel cards and passesare not. The government says they are not secure enough, a bit like saying a lock on the door is pointless because it’s not a burglar alarm.

There is a scheme for issuing free voter cards; applications must be in by April 25. People can also vote by post or by proxy without ID. And there are third parties pushing paid ID cards, too. But what it comes down to is next month a bunch of people are going to go to vote and will be barred. And this from the same people who wanted online voting to “increase access”.

Illustrations: London polling station 2017 (by Mramoeba at Wikimedia.

Wendy M. Grossman is the 2013 winner of the Enigma Award. Her Web site has an extensive archive of her books, articles, and music, and an archive of earlier columns in this series. Follow on Mastodon or Twitter.

Unclear and unpresent dangers

Monthly computer magazines used to fret that their news pages would be out of date by the time the new issue reached readers. This week in AI, a blog posting is out of date before you hit send.

This – Friday – morning, the Italian data protection authority, Il Garante, has ordered ChatGPT to stop processing the data of Italian users until it complies with the General Data Protection Regulation. Il Garante’s objections, per Apple’s translation, posted by Ian Brown: ChatGPT provides no legal basis for collecting and processing its massive store of the personal data used to train the model, and that it fails to filter out users under 13.

This may be the best possible answer to the complaint I’d been writing below.

On Wednesday, the Future of Life Institute published an open letter calling for a six-month pause on developing systems more powerful than Open AI’s current state of the art, GPT4. Barring Elon Musk, Steve Wozniack, and Skype co-founder Jaan Tallinn, most of the signatories are unfamiliar names to most of us, though the companies and institutions they represent aren’t – Pinterest, the MIT Center for Artificial Intelligence, UC Santa Cruz, Ripple, ABN-Amro Bank. Almost immediately, there was a dispute over the validity of the signatures..

My first reaction was on the order of: huh? The signatories are largely people who are inventing this stuff. They don’t have to issue a call. They can just *stop*, work to constrain the negative impacts of the services they provide, and lead by example. Or isn’t that sufficiently performative?

A second reaction: what about all those AI ethics teams that Silicon Valley companies are disbanding? Just in the last few weeks, these teams have been axed or cut at Microsoft and Twitch; Twitter of course ditched such fripperies last November in Musk’s inaugural wave of cost-cutting. The letter does not call to reinstate these.

The problem, as familiar critics such as Emily Bender pointed out almost immediately, is that the threats the letter focuses on are distant not-even-thunder. As she went on to say in a Twitter thread, the artificial general intelligence of the Singularitarian’s rapture is nowhere in sight. By focusing on distant threats – longtermism – we ignore the real and present problems whose roots are being continuously more deeply embedded into the new-building infrastructure: exploited workers, culturally appropriated data, lack of transparency around the models and algorithms used to build these systems….basically, all the ways they impinge upon human rights.

This isn’t the first time such a letter has been written and circulated. In 2015, Stephen Hawking, Musk, and about 150 others similarly warned of the dangers of the rise of “superintelligences”. Just a year later, in 2016, Pro Publica investigated the algorithm behind COMPAS, a risk-scoring criminal justice system in use in US courts in several states. Under Julia Angwin‘s scrutiny, the algorithm failed at both accuracy and fairness; it was heavily racially biased. *That*, not some distant fantasy, was the real threat to society.

“Threat” is the key issue here. This is, at heart, a letter about a security issue, and solutions to security issues are – or should be – responses to threat models. What is *this* threat model, and what level of resources to counter it does it justify?

Today, I’m far more worried by the release onto public roads of Teslas running Full Self Drive helmed by drivers with an inflated sense of the technology’s reliability than I am about all of human work being wiped away any time soon. This matters because, as Jessie Singal, author of There Are No Accidents, keeps reminding us, what we call “accidents” are the results of policy decisions. If we ignore the problems we are presently building in favor of fretting about a projected fantasy future, that, too, is a policy decision, and the collateral damage is not an accident. Can’t we do both? I imagine people saying. Yes. But only if we *do* both.

In a talk this week for a group at the French international research group AI Act. This effort began well before today’s generative tools exploded into public consciousness, and isn’t likely to conclude before 2024. It is, therefore, much more focused on the kinds of risks attached to public sector scandals like COMPAS and those documented in Cathy O’Neil’s 2017 book Weapons of Math Destruction, which laid bare the problems with algorithmic scoring with little to tether it to reality.

With or without a moratorium, what will “AI” look like in 2024? It has changed out of recognition just since the last draft text was published. Prediction from this biological supremacist: it still won’t be sentient.

All this said, as Edwards noted, even if the letter’s proposal is self-serving, a moratorium on development is not necessarily a bad idea. It’s just that if the risk is long-term and existential, what will six months do? If the real risk is the hidden continued centralization of data and power, then those six months could be genuinely destructive. So far, it seems like its major function is as a distraction. Resist.

Illustrations: IBM’s Watson, which beat two of Jeopardy‘s greatest champions in 2011. It has since failed to transform health care.

Memex 2.0

As language models get cheaper, it’s dawned on me what kind of “AI” I’d like to have: a fully personalized chat bot that has been trained on my 30-plus years of output plus all the material I’ve read, watched, listened to, and taken notes on all these years. A clone of my brain, basically, with more complete and accurate memory updated alongside my own. Then I could discuss with it: what’s interesting to write about for this week’s net.wars?

I was thinking of what’s happened with voice synthesis. In 2011, it took the Scottish company Cereproc months to build a text-to-speech synthesizer from recordings of Roger Ebert’s voice. Today, voice synthesizers are all over the place – not personalized like Ebert’s, but able to read a set text plausibly enough to scare voice actors.

I was also thinking of the Stochastic Parrots paper, whose first anniversary was celebrated last week by authors Emily Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell. An important part of the paper advocates for smaller, better-curated language models: more is not always better. I can’t find a stream for the event, but here’s the reading list collected during the proceedings. There’s lots I’d rather eliminate from my personal assistant. Eliminating unwanted options upfront has long been a widspread Internet failure, from shopping sites (“never show me pet items”) to news sites (“never show me fashion trends”). But that sort of selective display is more difficult and expensive than including everything and offering only inclusion filters.

A computational linguistics expert tells me that we’re an unknown amount of time away from my dream of the wg-bot. Probably, if such a thing becomes possible it will be based on someone’s large language model and fine-tuned with my stuff. Not sure I entirely like this idea; it means the model will be trained on stuff I haven’t chosen or vetted and whose source material is unknown, unless we get a grip on forcing disclosure or the proposed BLOOM academic open source language model takes over the world.

I want to say that one advantage to training a chatbot on your own output is you don’t have to worry so much about copyright. However, the reality is that most working writers have sold all rights to most of their work to large publishers, which means that such a system is a new version of digital cholera. In my own case, by the time I’d been in this business for 15 years, more than half of the publications I’d written for were defunct. I was lucky enough to retain at least non-exclusive rights to my most interesting work, but after so many closures and sales I couldn’t begin to guess – or even know how to find out – who owns the rights to the rest of it. The question is moot in any case: unless I choose to put those group reviews of Lotus 1-2-3 books back online, probably no one else will, and if I do no one will care.

On Mastodon, the specter of the upcoming new! improved! version of the copyright wars launched by the arrival of the Internet: “The real generative AI copyright wars aren’t going to be these tiny skirmishes over artists and Stability AI. Its going to be a war that puts filesharing 2.0 and the link tax rolled into one in the shade.” Edwards is referring to this case, in which artists are demanding billions from the company behind the Stable Diffusion engine.

Edwards went on to cite a Wall Street Journal piece that discusses publishers’ alarmed response to what they perceive as new threats to their business. First: that the large piles of data used to train generative “AI” models are appropriated without compensation. This is the steroid-fueled analogue to the link tax, under which search engines in Australia pay newspapers (primarily the Murdoch press) for including them in news search results. A similar proposal is pending in Canada.

The second is that users, satisfied with the answers they receive from these souped-up search services will no longer bother to visit the sources – especially since few, most notably Google, seem inclined to offer citations to back up any of the things they say.

The third is outright plagiarism without credit by the chatbot’s output, which is already happening.

The fourth point of contention is whether the results of generative AI should be themselves subject to copyright. So far, the consensus appears to be no, when it comes to artwork. But some publishers who have begun using generative chatbots to create “content” no doubt claim copyright in the results. It might make more sense to copyright the *prompt*. (And some bright corporate non-soul may yet try.)

At Walled Culture, Glyn Moody discovers that the EU has unexpectedly done something right by requiring positive opt-in to copyright protection against text and data mining. I’d like to see this as a ray of hope for avoiding the worst copyright conflicts, but given the transatlantic rhetoric around privacy laws and data flows, it seems much more likely to incite another trade conflict.

It now dawns on me that the system I outlined in the first paragraph is in fact Vannevar Bush’s Memex. Not the web, which was never sufficiently curated, but this, primed full of personal intellectual history. The “AI” represents those thousands of curating secretaries he thought the future would hold. As if.

Illustrations: Stable Diffusion rendering of “stochastic parrots”, as prompted by Jon Crowcroft.

Performing intelligence

“Oh, great,” I thought when news broke of the release of GPT-4. “Higher-quality deception.”

Most of the Internet disagreed; having gone mad only a few weeks ago over ChatGPT, everyone’s now agog over this latest model. It passed all these tests!

One exception was the journalist Paris Marx, who commented on Twitter: “It’s so funny to me that the AI people think it’s impressive when their programs pass a test after being trained on all the answers.”

Agreed. It’s also so funny to me that they call that “AI” and don’t like it when researchers like computational linguist Emily Bender call it a “stochastic parrot”. At Marx’s Tech Won’t Save Us podcast, Goldsmith professor Dan McQuillan, author of Resisting AI: An Anti-fascist Approach to Artificial Intelligence, calls it a “bullshit engine” whose developers’ sole goal is plausibility – plausibility that, as Bender has said, allows us imaginative humans to think we detect a mind behind it, and the result is to risk devaluing humans.

Let’s walk back to an earlier type of system that has been widely deployed: benefits scoring systems. A couple of weeks ago, Lighthouse Reports and Wired magazine teamed up on an investigation of these systems, calling them “suspicion machines”.

Their work focuses on the welfare benefits system in use in Rotterdam between 2017 and 2021, which uses 315 variables to risk-score benefits recipients according to the likelihood that their claims are fraudulent. In detailed, worked case analyses, they find systemic discrimination: you lose points for being female, for being female and having children (males aren’t asked about children), for being non-white, and for ethnicity (knowing Dutch a requirement for welfare recipients). Other variables include missing meetings, age, and “lacks organizing skills”, which was just one of 54 variables based on case workers’ subjective assessments. Any comment a caseworker adds translates to a 1 added to the risk score, even if it’s positive. The top-scoring 10% are flagged for further investigation.

This is the system that Accenture, the city’s technology partner on the early versions, said at its unveiling in 2018 was an “ethical solution” and promised “unbiased citizen outcomes”. Instead, Wired says, the algorithm “fails the city’s own test of fairness”.

The project’s point wasn’t to pick on Rotterdam; of the dozens of cities they contacted it just happened to be the only one that was willing to share the code behind the algorithm, along with the list of variables, prior evaluations, and the data scientists’ handbook. It even – after being threatened with court action under freedom of information laws, shared the mathematical model itself.

The overall conclusion: the system was so inaccurate it was little better than random sampling “according to some metrics”.

What strikes me, aside from the details of this design, is the initial choice of scoring benefits recipients for risk of fraud. Why not score them for risk of missing out on help they’re entitled to? The UK government’s figures on benefits fraud indicate that in 2021-2022 overpayment (including error as well as fraud) amounted to 4%; and *underpayment* 1.2% of total expenditure. Underpayment is a lot less, but it’s still substantial (£2.6 billion). Yes, I know, the point of the scoring system is to save money, but the point of the *benefits* system is to help people who need it. The suspicion was always there, but the technology has altered the balance.

This was the point the writer Ellen Ullman noted in her 1996 book Close to the Machine”: the hard-edged nature of these systems and their ability to surveil people in new ways, “infect” their owners with suspicion even of people they’ve long trusted and even when the system itself was intended to be helpful. On a societal scale, these “suspicion machines” embed increased division in our infrastructure; in his book, McQuillan warns us to watch for “functionality that contributes to violent separations of ‘us and them’.”

Along those lines, it’s disturbing that Open AI, the owner of ChatGPT and GPT-4 (and several other generative AI gewgaws) has now decided to keep secret the details of its large language models. That is, we have no sight into what data was used in training, what software and hardware methods were used, or how energy-intensive it is. If there’s a machine loose in the world’s computer systems pretending to be human, shouldn’t we understand how it works? It would help with damping down imagining we see a mind in there.

The company’s argument appears to be that because these models could become harmful it’s bad to publish how they work because then bad actors will use them to create harm. In the cybersecurity field we call this “security by obscurity” and there is a general consensus that it does not work as a protection.

In a lengthy article at New York magazine, Elizabeth Weil. quotes Daniel Dennett’s assessment of these machines: “counterfeit people” that should be seen as the same sort of danger to our system as counterfeit money. Bender suggests that rather than trying to make fake people we should be focusing more on making tools to help people.

The thing that makes me tie it to the large language models that are producing GPT is that in both cases it’s all about mining our shared cultural history, with all its flaws and misjudgments, in response to a prompt and pretending the results have meaning and create new knowledge. And *that’s* what’s being embedded into the world’s infrastructure. Have we learned nothing from Clever Hans?

Illustrations: Clever Hans, performing in Leipzig in 1912 (by Karl Krali, via Wikimedia.

Gap year

What do Internet users want?

First, they want meaningful access. They want usability. They want not to be scammed, manipulated, lied to, exploited, or cheated.

It’s unlikely that any of the ongoing debates in either the US or UK will deliver any of those.

First and foremost, this week concluded two frustrating years in which the US Senate failed to confirm the appointment of Public Knowledge co-founder and EFF board member Gigi Sohn to the Federal Communications Commission. In her withdrawal statement, Sohn blamed a smear campaign by “legions of cable and media industry lobbyists, their bought-and-paid-for surrogates, and dark money political groups with bottomless pockets”.

Whether you agree or not, the result remains that for the last two years and for the foreseeable future the FCC will remain deadlocked and problems such as the US’s lack of competition and patchy broadband provision will remain unsolved.

Meanwhile, US politicians continue obsessing about whether and how to abort-retry-fail Section 230, that pesky 26-word law that relieves Internet hosts of liability for third-party content. This week it was the turn of the Senate Judiciary Committee. In its hearing, the Internet Society’s Andrew Sullivan stood out for trying to get across to lawmakers that S230 wasn’t – couldn’t have been – intended as protectionism for the technology giants because they did not exist when the law was passed. It’s fair to say that S230 helped allow the growth of *some* Internet companies – those that host user-generated content. That means all the social media sites as well as web boards and blogs and Google’s search engine and Amazon’s reviews, but neither Apple nor Netflix makes its living that way. Attacking the technology giants is a popular pasttime just now, but throwing out S230 without due attention to the unexpected collateral damage will just make them bigger.

Also on the US political mind is a proposed ban on TikTok. It’s hard to think of a move that would more quickly alienate young people. Plus, it fails to get at the root problem. If the fear is that TikTok gathers data on Americans and sends it home to China for use in designing manipulative programs…well, why single out TikTok when it lives in a forest of US companies doing the same kind of thing? As Karl Bode writes at TechDirt, if you really want to mitigate that threat, rein in the whole forest. Otherwise, if China really wants that data it can buy it on the open market.

Meanwhile, in the UK, as noted last week, opposition continues to increase to the clauses in the Online Safety bill proposing to undermine end-to-end encryption by requiring platforms to proactively scan private messages. This week, WhatsApp said it would withdraw its app from the UK rather than comply. However important the UK market is, it can’t possibly be big enough for Meta to risk fines of 4% of global revenues and criminal sanctions for executives. The really dumb thing is that everyone within the government uses WhatsApp because of its convenience and security, and we all know it. Or do they think they’ll have special access denied the rest of the population?

Also in the UK this week, the Data Protection and Digital Information bill returned to Parliament for its second reading. This is the UK’s post-Brexit attempt to “take control” by revising the EU’s General Data Protection Regulation; it was delayed during Liz Truss’s brief and destructive outing as prime minister. In its statement, the government talks about reducing the burdens on businesses without any apparent recognition that divergence from GDPR is risky for anyone trading internationally and complying with two regimes must inevitably be more expensive than complying with one.

The Open Rights Group and 25 other civil society organizations have written a letter (PDF) laying out their objections, noting that the proposed bill, in line with other recent legislation that weakens civil rights, weakens oversight and corporate accountability, lessens individuals’ rights, and weakens the independence of the Information Commissioner’s Office. “Co-designed with businesses from the start” is how the government describes the bill. But data protection law was not supposed to be designed for business – or, as Peter Geoghegan says at the London Review of Books, to aid SLAPP suits; it is supposed to protect our human rights in the face of state and corporate power. As the cryptography pioneer Whit Diffie said in 2019, “The problem isn’t privacy; it’s corporate malfeasance.”

The most depressing thing about all of these discussions is that the public interest is the loser in all of them. It makes no sense to focus on TikTok when US companies are just as aggressive in exploiting users’ data. It makes no sense to focus solely on the technology giants when the point of S230 was to protect small businesses, non-profits, and hobbyists. And it makes no sense to undermine the security afforded by end-to-end encryption when it’s essential for protecting the vulnerable people the Online Safety bill is supposed to help. In a survey, EDRi finds that compromising secure messaging is highly unpopular with young people, who clearly understand the risks to political activism and gender identity exploration.

One of the most disturbing aspects of our politics in this century so far is the widening gap between what people want, need, and know and the things politicians obsess about. We’re seeing this reflected in Internet policy, and it’s not helpful.

Illustrations: Andrew Sullivan, president of the Internet Society, testifying in front of the Senate Judiciary Committee.

Ghostwritten

This week’s deliberate leak of 100,000 WhatsApp messages sent between the retiring MP Matt Hancock (Con-West Suffolk) and his cabinet colleagues and scientific advisers offers several lessons for the future. Hancock was the health minister during the first year of the covid-19 pandemic, but forced to resign in June 2021, when he was caught on a security camera snogging an adviser in contravention of the social distancing rules.

The most ignored lesson relates to cybersecurity, and is simple: electronic messages are always at risk of copying and disclosure.

This leak happened to coincide with the revival of debates around the future of strong encryption in the UK. First, the pending Online Safety bill has provisions that taken together would undermine all encrypted communications. Simultaneously, a consultation on serious and organized crime proposes to criminalize “custom” encryption devices. A “dictionary attack”, Tim Cushing calls this idea at Techdirt, in that the government will get to define the crime at will.

The Online Safety Bill is the more imminent problem; it has already passed the House of Commons and is at the committee stage in the House of Lords. The bill requires service providers to protect children by proactively removing harmful content, whether public or private, and threatens criminal liability for executives of companies that fail to comply.

Signal, which is basically the same as WhatsApp without the Facebook ownership, has already said it will leave the country if the Online Safety bill passes with the provisions undermining encryption intact.

It’s hard to see what else Signal could do. It’s not a company that has to weigh its principles against the loss of revenue. Instead, as a California non-profit, its biggest asset is the trust of its user base, and staying in a country that has outlawed private communications would kill that off at speed. In threatening to leave it has company: the British secure communications company Element, which said the provisions would taint any secure communications product coming out of the UK – presumably even for its UK customers, such as the Ministry of Defence.

What the Hancock leak reminds us, however, is that encryption, even when appropriately strong and applied end-to-end, is not enough by itself to protect security. You must also be able to trust everyone in the chain to store the messages safely and respect their confidentiality. The biggest threat is careless or malicious insiders, who can undermine security in all sorts of ways. Signal (as an example) provides the ability to encrypt the message database, to disappear messages on an automated schedule, password protection, and so on. If you’re an activist in a hostile area, you may be diligent about turning all these on. But you have no way of knowing if your correspondents are just as careful.

In the case at hand, Hancock gave the messages to the ghost writer for his December 2022 book Pandemic Diaries, Isabel Oakeshott, after requiring her to sign a non-disclosure agreement that he must have thought would protect him, if not his colleagues, from unwanted disclosures. Oakeshott, who claims she acted in the public interest, decided to give the messages to the Daily Telegraph, which is now mining them for stories.

Digression: whatever Oakeshott’s personal motives, there is certainly public interest in these messages. The tone of many quoted exchanges confirms the public perception of the elitism and fecklessness of many of those in government. More interesting is the close-up look at decision making in conditions of uncertainty, which to some filled with hindsight looks like ignorance and impatience. It’s astonishing how quickly people have forgotten how much we didn’t know. As mathematician Christina Pagel told the BBC’s Newsnight, you can’t wait for more evidence when the infection rate is doubling every four days.

What they didn’t know and when they didn’t know it will be an important part of piecing together what actually happened. The mathematician Kit Yates has dissected another exchange, in which Boris Johnson queries his scientific advisers about fatality rates. Yates argues that in assessing this exchange timing ise everything. Had it been in early 2020, it would be understandable to confuse infection fatality rates and case fatality rates, though less so to confuse fractions (0.04) and percentages (4%). Yates pillories Johnson because in fact that exchange took place in August 2020, by which time greater knowledge should have conferred greater clarity. That said, security people might find familiar Johnson’s behavior in this exchange, where he appears to see the Financial Times as a greater authority than the scientists. Isn’t that just like every company CEO?

Exchanges like that are no doubt why the participants wanted the messages kept private. In a crisis, you need to be able to ask stupid questions. It would be better to have a prime minister who can do math and who sweats the details, but if that’s not what we’ve got I’d rather he at least asked for clarification.

Still, as we head into yet another round of the crypto wars, the bottom line is this: neither technology nor law prevented these messages from leaking out some 30 years early. We need the technology. We need the law on our side. But even then, your confidences are only ever as private as your correspondent(s) and their trust network(s) will allow.

Illustrations: The soon-to-be-former-MP Matt Hancock, on I’m a Celebrity.

A world of lawsuits

In the US this week the Supreme Court heard arguments in two cases centered on Section 230, the US law that shields online platforms from liability for third-party content. In Paris, UNESCO convened Internet for Trust to bring together governments and civil society to contemplate global solutions to the persistent problems of Internet regulation. And in the business of cyberspace, in what looks like desperation to stay afloat Twitter began barring non-paying users (that is, the 99.8% of its user base that *doesn’t* subscribe to Twitter Blue) from using two-factor authentication via SMS and Meta announced plans for a Twitter Blue-like subscription service for its Facebook, Instagram, and WhatsApp platforms.

In other words, the above policy discussions are happening exactly at the moment when, for the first time in nearly two decades, two of the platforms whose influence everyone is most worried about may be beginning to implode. Twitter’s issues are well-known. Meta’s revenues are big enough that there’s a long way for them to fall…but the company is spending large fortunes on developing the Metaverse, which no one may want, and watching its ad sales shrink and data protection fines rise.

The SCOTUS hearings – Gonzalez v. Google, experts’ live blog, Twitter v. Taamneh – have been widely covered in detail. In most cases, writers note that trying to discern the court’s eventual ruling from the justices’ questions is about as accurate as reading tea leaves. Nonetheless, Columbia professor Tim Wu predicts that Gonzalez will lose but that Taamneh could be very close.

In Gonzalez, the parents of a 23-year-old student killed in a 2015 ISIS attack in Paris argue that YouTube should be liable for radicalizing individuals via videos found and recommended on its platform. In Taamneh, the family of a Jordanian citizen who died in a 2017 ISIS attack in Istanbul sued Twitter, Google, and Facebook for failing to control terrorist content on their sites under anti-terrorism laws. A ruling assigning liability in either case could be consequential for S230. At TechDirt, Mike Masnick has an excellent summary of the Gonzalez hearing, as well as a preview of both cases.

Taamneh, on the other hand, asks whether social media sites are “aiding and abetting” terrorism via their recommendations engines under Section 2333 of the Antiterrorism and Effective Death Penalty Act (1996). Under the Justice Against Sponsors of Terrorism Act (2016) any US national who is injured by an act of international terorrism can sue anyone who “aids and abets by knowingly providing substantial assistance” to anyone committing such an act. The case turns on how much Twitter knows about its individual users and what constitutes substantial assistance. There has been some concern, expressed in amicus briefs, that making online intermediaries liable for terrorist content will result in overzealous content moderation. Lawfare has a good summary of the cases and the amicus briefs they’ve attracted.

Contrary to what many people seem to think, while S230 allows content moderation, it’s not a law that disproportionately protects large platforms, which didn’t exist when it was enacted. As Kosseff tells Gizmodo: without liability protection a local newspaper or personal blog could not risk publishing reader comments, and Wikipedia could not function. Justice Elena Kagan has been mocked for saying the justices are “not the nine greatest experts on the Internet”, but she grasped perfectly that undermining S230 could create “a world of lawsuits”.

For the last few years, both Democrats and Republicans have called for S230 reform, but for different reasons. Democrats fret about the proliferation of misinformation; Republicans complain that they (“conservative voices”) are being censored. The global level seen at the UNESCO event took a broader view in trying to draft a framework for self-regulation. While it wouldn’t be binding, there’s some value in having an multi-stakeholder-agreed standard against which individual governmental proposals can be evaluated. One of the big gaps in the UK’s Online Safety bill;, for example, is the failure to tackle misinformation or disinformation campaigns. Neither reforming S230 nor a framework for self-regulation will solve that problem either: over the last few years too much of the most widely-disseminated disinformation has been posted from official accounts belonging to world leaders.

One interesting aspect is how many new types of “content” have been created since S230’s passage in 1996, when the dominant web analogy was print publishing. It’s not just recommendation algorithms; are “likes” third-party content? Are the thumbnails YouTube’s algorithm selects to show each visitor on its front page to entice viewers presentation or publishing?

In his biography of S230, The Twenty-Six Words That Created the Internet, Jeff Kosseff notes that although similar provisions exist in other legislation across the world, S230 is unique in that only America privileges freedom of speech to such an extreme extent. Most other countries aim for more of a balance between freedom of expression and privacy. In 1997, it was easy to believe that S230 enabled the Internet to export the US’s First Amendment around the world like a stowaway. Today, it seems more like the first answer to an eternally-recurring debate. Despite its problems, like democracy itself, it may continue to be the least-worst option.

Illustrations: US senator and S230 co-author Ron Wyden (D-OR) in 2011 (by JS Lasica via Wikimedia.

Wendy M. Grossman is the 2013 winner of the Enigma Award. Her Web site has an archive of earlier columns backj to 2001. Follow on Mastodon or Twitter.

Esquivalience

The science fiction author Charles Stross had a moment of excitement on Mastodon this week: WRITER CHALLENGE!.

Stross challenged writers to use the word “esquivalience” in their work. The basic idea: turn this Pinocchio word into a “real” word.

Esquivalience is the linguistic equivalent of a man-made lake. The creator, editor Christine Lindberg, invented it for the 2001 edition of the New American Oxford Dictionary and defined it as “the willful avoidance of one’s official responsibilities; the shirking of duties”. It was a trap to catch anyone republishing the dictionary rather than developing their own (a job I have actually done). This is a common tactic for protecting large compilations where it’s hard to prove copying – fake streets are added to maps, for example, and the people who rent out mailing lists add ringers whose use will alert them if the list is used outside the bounds of the contractual agreement.

There is, however, something peculiarly distasteful about fake entries in supposedly authoritative dictionaries, even though I agree with Lindberg that “esquivalience” is a pretty useful addition to the language. It’s perfect – perhaps in the obvious adjectival form “esquivalient” – for numerous contemporary politicians, though here be dragons: “willful” risks libel actions.

Probably most writers have wanted to make up words, and many have, from playwright and drama critic George S. Kaufman, often credited for coining, among other things, “underwhelmed”, to Anthony Burgess, who invented an entire futurist street language for A Clockwork Orange. Some have gone so far as to create enough words to publish dictionaries – such as the humorist Gelett Burgess, whose Burgess Unabridged (free ebook!) compiles “words you’ve always needed”. From that collection, I have always been particularly fond of Burgess’s “wox”, defined as “a state of placid enjoyment; sluggish satisfaction”. It seems particularly apt in the hours immediately following Thanksgiving dinner.

In these cases, though, the context lets you know the language is made up. The dictionary is supposed to be authoritative, admitting words only after they are well-established. The presence of fake words feels damaging in a way that a fake place on a map doesn’t. It’s comparatively easy to check whether a place exists by going there, but at some point down the echoing corridors of time *every* word was used for the first time. Pinpointing exactly when is hard unless someone ‘fesses up. I don’t like the idea that my dictionary is lying to me. Better if NOAD had planted two fake words and had them recursively point at each other for their definitions.

I had been avoiding the ChatGPT hoopla, but it seemed plausible to ask it: Is “esquivalience” a real word?” Its response started well enough: “‘Esquivalience’ is not recognized as a standard word in the English language. It is a made-up word…” And then cuckoo land arrived: “…that was created by a writer named Adam Jacot de Boinod for his book “The Meaning of Tingo”.” Pause to research. The book in question was written in 2006. The word “esquivalience” does not, from a quick text search, appear in it. Huh? I went on to suggest Christine Lindberg’s name to ChatGPT, and after a digression attributing the word to the singer-songwriter Christine Lavin, it appeared to find references to Lindberg’s “claim” in its corpus of data. But, it continued to warn, in every response, “it is still not recognized as a standard word in the English language”. It’s a bot. It’s not being stern. It doesn’t know what it’s saying. Getting it to agree on Christine Lindberg as the original source isn’t winning the argument. It’s just giving it a different prompt.

I ask if it has ever encountered the word “wox”. “As an AI language model, I have certainly come across the word ‘wox’.” A human reads lightly insulted pride into that. Resist. It’s a bot. It has no pride. The bot went on to speculate on possible origins (“it may be a neologism…”). I ask if it’s heard of Gelett Burgess. Oh, yes, followed by a short biography. Then, when told Burgess invented “wox”: “Gelett Burgess did indeed invent the word…” and goes on to cite the correct book…but then continues that Burgess defined it as “to make fun of, to poke fun at” which is absolutely not what Burgess says, and I know this because I have the original 1914 book right here, and the definition I cited above is right there on p112. The bot does “apologize” every time you point out a mistake, though.

This isn’t much of a sample, but based on it, I find ChatGPT quite alarming as an extraordinarily efficient way of undermining factual knowledge. The responses sound authoritative, but every point must be fact-checked. It could not be worse-suited for today’s world, where everyone wants fast answers. Coupled with search, it turns the algorithms that give us answers into even more obscure and less trustworthy black boxes. Wikipedia has many flaws, but its single biggest strength is its sourcing and curation; how every page has been changed and shaped over the years is open for inspection.

So when ChatGPT went on to say that Gelett Burgess is widely credited with coining the term “blurb”, Wikipedia is where I turned. Wikipedia agrees (asked, ChatGPT cites the Oxford English Dictionary). Burgess FTW.

Illustrations: Gelett Burgess’s 1914 Burgess Unabridged, a dictionary of made-up words.

Review: Survival of the Richest

A former junior minister who’d been a publicity magnet while in office once told me that it’s impossible to travel on the tube when you’re famous – except in morning rush hour, when everyone glumly hides behind their newspaper. (This was some while ago, before smartphones.)

It was the first time I’d realized that if you were going to be famous it was wise to also be rich enough to buy yourself some personal space. The problem we face today is that we have multi-billionaires who are so rich that they can surround themselves with nothing *but* personal space, and rain in the form of other people never falls into their lives.

In fact, as Douglas Rushkoff writes in Survival of the Richest, this class of human sees the rest of us as an impediment to their own survival. Instead, they want to extract everything they can from us and then achieve escape velocity as completely as possible.

Rushkoff came to realize this when he was transported far out into the American southwestern desert by a pentangle of multi-billionaires who wanted advice: what, in his opinion, was the best way to hide from out and survive various prospective catastrophes (“The Event”)? Climate change, pandemics, mass migration, and resource depletion – where to go and for how long? Alaska, New Zealand, Mars, or the Metaverse: all their ideas about the future involved escaping humanity. Except: what, one wanted to know, would be the best way to keep control of their private security force?

This was the moment when Rushkoff discovered what he calls “The Mindset”, whose origins and development are what the book is really about. It is, he writes, “a mindset where ‘winning’ means earning enough money to insulate themselves from the damage they are creating by earning money in that way. It’s as if they want to build a car that goes fast enough to escape from its own exhaust”. The Mindset is a game – and a game needs an end: in this case, a catastrophe they can invent a technology to escape.

He goes on to tease out the elements of The Mindset: financial abstraction, Richard Dawkins’ memes that see humans as machines running code with no pesky questions of morals, technology design, the type of philanthropy that hands out vaccines but refuses to waive patents so lower-income countries can make them. The Mindset comprehends competition, but not collaboration even though, as Rushkoff notes, our greatest achievement, science, is entirely collaborative.

‘Twas not ever thus. Go back to Apple’s famous 1984 Super Bowl ad and recall the promise that ushered in the first personal computers: empower the masses and destroy the monolith (at the time, IBM). Now, the top 0.1% compete to “win” control of all they survey, the top 1% scrabble for their pocket change, and the rest subsist on whatever is too small for them to notice. This is not the future we thought we were buying into.

As Rushkoff concludes, the inevitability narrative that accompanies so much technological progress is nonsense. We have choices. We can choose to define value in social terms rather than exit strategies. We can build companies and services – and successful cooperatives – to serve people and stop expanding them when they reach the size that fits their purpose. We do not have to believe today’s winners when they tell us a more equitable world is impossible. We don’t need escape fantasies; we can change reality.