net.wars

Review: The Web We Weave

The Web We Weave
By Jeff Jarvis
Basic Books
ISBN: 9781541604124

Sometime in the very early 1990s, someone came up to me at a conference and told me I should read the work of Robert McChesney. When I followed the instruction, I found a history of how radio and TV started as educational media and wound up commercially controlled. Ever since, this is the lens through which I’ve watched the Internet develop: how do we keep the Internet from following that same path? If all you look at is the last 30 years of web development, you might think we can’t.

A similar mission animates retired CUNY professor Jeff Jarvis in his latest book, The Web We Weave. In it, among other things, he advocates reanimating the open web by reviving the blogs many abandoned when Twitter came along and embracing other forms of citizen media. Phenomena such as disinformation, misinformation, and other harms attributed to social media, he writes, have precursor moral panics: novels, comic books, radio, TV, all were once new media whose evils older generations fretted about. (For my parents, it was comic books, which they completely banned while ignoring the hours of TV I watched.) With that past in mind, much of today’s online harms regulation leaves him skeptical.

As a media professor, Jarvis is interested in the broad sweep of history, setting social media into the context that began with the invention of the printing press. That has its benefits when it comes to later chapters where he’s making policy recommendations on what to regulate and how. Jarvis is emphatically a free-speech advocate.

Among his recommendations are those such advocates typically support: users should be empowered, educated, and taught to take responsibility, and we should develop business models that support good speech. Regulation, he writes, should include the following elements: transparency, accountability, disclosure, redress, and behavior rather than content.

On the other hand, Jarvis is emphatically not a technical or design expert, and therefore has little to say about the impact on user behavior of technical design decisions. Some things we know are constants. For example, the willingness of (fully identified) online communicators to attack each other was noted as long ago as the 1980s, when Sara Kiesler studied the first corporate mailing lists.

Others, however, are not. Those developing Mastodon, for example, deliberately chose not to implement the ability to quote and comment on a post because they believed that feature fostered abuse and pile-ons. Similarly, Lawrence Lessig pointed out in 1999 in Code and Other Laws of Cyberspae (PDF) that you couldn’t foment a revolution using AOL chatrooms because they had a limit of 23 simultaneous users.

Understanding the impact of technical decisions requires experience, experimentation, and, above all, time. If you doubt this, read Mike Masnick’s series at Techdirt on Elon Musk’s takeover and destruction of Twitter. His changes to the verification system alone have undermined the ability to understand who’s posting and decide how trustworthy their information is.

Jarvis goes on to suggest we should rediscover human scale and mutual obligation, both crucial as the covid pandemic progressed. The money will always favor mass scale. But we don’t have to go that way.

Review: Supremacy

Supremacy: AI, ChatGPT, and the Race That Will Change the World
By Parmy Olson
Macmillan Business
ISBN: 978-1035038220

One of the most famous books about the process of writing software is Frederick Brooks’ The Mythical Man Month. The essay that gives the book its title makes the point that you cannot speed up the process by throwing more and more people at it. The more people you have, the more they have to all communicate with each other, and the pathways multiply exponentially. Think of it this way: 500 people can’t read a book faster than five people can.

Brooks’ warning immediately springs to mind when Parmy Olson reports, late in her new book, Supremacy, that Microsoft CEO Sadya Nadella was furious to discover that Microsoft’s 5,000 direct employees working on AI lagged well behind the rapid advances being made by the fewer than 200 working working at OpenAI. Some things just aren’t improved by parallel processing.

The story Olson tells is a sad one: two guys, both eager to develop an artificial general intelligence in order to save, or least help, humanity, who both wind up working for large commercial companies whose primary interests are to 1) make money and 2) outcompete the other guy. For Demis Hassabis, the company was Google, which bought his DeepMind startup in 2014. For Sam Altman, founder of OpenAI, it was Microsoft. Which fits: Hassabis’s approach to “solving AI” was to let them teach themselves by playing games, hoping to drive science discovery; Altman sought to solve real-world problems and bring everyone wealth. Too late for Olson’s book, Hassabis has achieved enough of a piece of his dream to have been one of three awarded the 2024 Nobel Prize in chemistry for using AI to predict how proteins will fold.

For both the reason was the same: the resources they sought to work in AI – data, computing power, and high-priced personnel – are too expensive for either traditional startup venture capital funding or for academia. (Cure Vladen Joler, at this year’s Computers, Privacy, and Data Protection, noting that AI is arriving “pre-monopolized”.) As Olson tells the story, they both tried repeatedly to keep the companies they founded independent. Yet, both have wound up positioned to run the companies whose money they took apparently believing, like many geek founders with more IQ points than sense, that they would not have to give up control.

In comparing and contrasting the two founders, Olson shows where many of today’s problems came from. Allying themselves with Big Tech meant giving up on transparency. The ethicists who are calling out these companies over real and present harms caused by the tools they’ve built, such as bias, discrimination, and exploitation of workers performing tasks like labeling data, have 1% or less of the funding of those pushing safety for superintelligences that may never exist.

Olson does a good job of explaining the technical advances that led to the breakthroughs of recent years, as well as the business and staff realities of their different paths. A key point she pulls out is the extent to which both Google and Microsoft have become the kind of risk-averse, slow-moving, sclerotic company they despised when they were small, nimble newcomers.

Different paths, but ultimately, their story is the same: they fought the money, and the money won.

This perfect day

To anyone remembering the excitement over DNA testing just a few years ago, this week’s news about 23andMe comes as a surprise. At CNN, Allison Morrow reports that all seven board members have resigned to protest CEO Anne Wojcicki’s plan to take the company private by buying up all the shares she doesn’t already own at 40 cents each (closing price yesterday was 0.3301. The board wanted her to find a buyer offering a better price.

In January, Rolfe Winkler reported at the Wall Street Journal ($) that 23andMe is likely to run out of cash by next year. Its market cap has dropped from $6 billion to under $200 million. He and Morrow catalogue the company’s problems: it’s never made a profit nor had a sustainable business model.

The reasons are fairly simple: few repeat customers. With DNA testing, as Winkler writes, “Customers only need to take the test once, and few test-takers get life-altering health results.” 23andMe’s mooted revolution in health care instead was a fad. Now, the company is pivoting to sell subscriptions to weight loss drugs.

This strikes me as an extraordinarily dangerous moment: the struggling company’s sole unique asset is a pile of more than 10 million DNA samples whose owners have agreed they can be used for research. Many were alarmed when, in December 2023, hackers broke into 1.7 million accounts and gained access to 6.9 million customer profiles<, though. The company said the hacked data did not include DNA records but did include family trees and other links. We don't think of 23andMe as a social network. But the same affordances that enabled Cambridge Analytica to leverage a relatively small number of user profiles to create a mass of data derived from a much larger number of their Friends worked on 23andMe. Given the way genetics works, this risk should have been obvious.

In 2004, the year of Facebook’s birth, the Australian privacy campaigner Roger Clarke warned in Very Black “Little Black Books” that social networks had no business model other than to abuse their users’ data. 23andMe’s terms and conditions promise to protect user privacy. But in a sale what happens to the data?

The same might be asked about the data that would accrue from Oracle CEO Larry Ellison‘s surveillance-embracing proposals this week. Inevitably, commentators invoked George Orwell’s 1984. At Business Insider, Kenneth Niemeyer was first to report: “[Ellison] said AI will usher in a new era of surveillance that he gleefully said will ensure ‘citizens will be on their best behavior.'”

The all-AI-surveillance all-the-time idea could only be embraced “gleefully” by someone who doesn’t believe it will affect him.

Niemeyer:

“Ellison said AI would be used in the future to constantly watch and analyze vast surveillance systems, like security cameras, police body cameras, doorbell cameras, and vehicle dashboard cameras.

“We’re going to have supervision,” Ellison said. “Every police officer is going to be supervised at all times, and if there’s a problem, AI will report that problem and report it to the appropriate person. Citizens will be on their best behavior because we are constantly recording and reporting everything that’s going on.”

Ellison is twenty-six years behind science fiction author David Brin, who proposed radical transparency in his 1998 non-fiction outing, The Transparent Society. But Brin saw reciprocity as an essential feature, believing it would protect privacy by making surveillance visible. Ellison is claiming that *inscrutable* surveillance will guarantee good behavior.

At 404 Media, Jason Koebler debunks Ellison point by point. Research and other evidence shows securing schools is unlikely to make them safer; body cameras don’t appear to improve police behavior; and all the technologies Ellison talks about have problems with accuracy and false positives. Indeed, the mayor of Chicago wants to end the city’s contract with ShotSpotter (now SoundThinking), saying it’s expensive and doesn’t cut crime; some research says it slows police 911 response. Worth noting Simon Spichak at Brain Facts, who finds with AI tools humans make worse decisions. So…not a good idea for police.

More disturbing is Koebler’s main point: most of the technology Ellison calls “future” is already here and failing to lower crime rates or solve its causes – while being very expensive. Ellison is already out of date.

The book Ellison’s fantasy evokes for me is the less-known This Perfect Day, by Ira Levin, written in 1970. The novel’s world is run by a massive computer (“Unicomp”) that decides all aspects of individuals’ lives: their job, spouse, how many children they can have. Enforcing all this are human counselors and permanent electronic bracelets individuals touch to ubiquitous scanners for permission.

Homogeneity rules: everyone is mixed race, there are only four boys’ and girls’ names, they eat “totalcakes”, drink cokes, wear identical clothing. For the rest, regularly administered drugs keep everyone healthy and docile. “Fight” is an abominable curse word. The controlled world over which Unicomp presides is therefore almost entirely benign: there is no war, crime, and little disease. It rains only at night.

Naturally, the novel’s hero rebels, joins a group of outcasts (“the Incurables”), and finds his way to the secret underground luxury bunker where a few “Programmers” help Unicomp’s inventor, Wei Li Chun, run the world to his specification. So to me, Ellison’s plan is all about installing himself as world ruler. Which, I mean, who could object except other billionaires?

Illustrations: The CCTV camera on George Orwell’s Portobello Road house.

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. She is a contributing editor for the Plutopia News Network podcast. Follow on Mastodon.

Service Model

Service Model
By Adrian Tchaikovsky
Tor Publishing Group
ISBN: 978-1-250-29028-1

Charles is a highly sophisticated robot having a bad day. As a robot, “he” would not express it that way. Instead, he would say that he progresses through each item on his task list and notes its ongoing pointlessness. He checks his master’s travel schedule and finds no plans, Nonetheless, he completes his next tasks, laying out today’s travel clothes, dusting off yesterday’s unused set, and placing them back in the wardrobe, as he has every day for the 2,230 days since his master last left the house.

He goes on to ask House, the manor’s major-domo system, to check with the lady of the house’s maidservant for travel schedules, planned clothing, and other aspects of life. There has been no lady of the house, and therefore no maidservant, for 17 years and 12 days. An old subroutine suggests ways to improve efficiency by eliminating some of the many empty steps, but Charles has no instructions that would let him delete any of them, even when House reports errors. The morning routine continues. It’s tempting to recall Ray Bradbury’s short story “There Will Come Soft Rains”.

Until Charles and House jointly discover there are red stains on the car upholstery Charles has just cleaned…and on Charles’s hands, and on the master’s laid-out clothes, and on his bedclothes and on his throat where Charles has recently been shaving him with a straight razor…

The master has been murdered.

So begins Adrian Tchaikovsky’s post-apocalyptic science fiction novel Service Model.

Some time later – after a police investigation – Charles sets out to walk the long miles to report to Diagnostics, and perhaps thereafter to find a new master in need of a gentleman’s gentlebot. Charles would not say he “hoped”; he would say he awaits instructions, and that the resulting uncertainty is inefficiently consuming his resources.

His journey takes him through a landscape filled with other robots that have lost their purpose. Manor after manor along the road is dark or damaged; at one, a servant robot waits pointlessly to welcome guests who never come. The world, it seems, is stuck in recursive loops that cannot be overridden because the human staff required to do so have been…retired. At the Diagnostics center Charles finds more of the same: a queue of hundreds of robots waiting to be seen, stalled by the lack of a Grade Seven human to resolve the blockage.

Enter “the Wonk”, a faulty robot with no electronic link and a need to recharge at night and consume food, who sees Charles – now Uncharles, since he no longer serves the master who named him – as infected with the “protagonist virus” and wants him to join in searching for the mysterious Library, which is preserving human knowledge. Uncharles is more interested in finding humans he can serve.

Their further explorations of a post-apocalyptic world, thinly populated and filled with the rubble of cities, along with Uncharles’s efforts to understand his nature, form most of the rest of the book. Is Wonk’s protagonist virus even a real thing? He doubts that it is. And yet, he feels himself finding excuses to avoid taking on yet another pointless job.

The best part of all this is Tchaikovsky’s rendering of Cbarles/Uncharles’s thoughts about himself and his attempts to make sense of the increasingly absurd world around him. A long, long way into the book it’s still not obvious how it will end.

Review: Money in the Metaverse

Money in the Metaverse: Digital Assets, Online Identities, Spatial Computing, and Why Virtual Worlds Mean Real Business
by David Birch and Victoria Richardson
London Publishing Partnership
ISBN: 978-1-916749-05-4

In my area of London there are two buildings whose architecture unmistakably identifies them as former banks. Time has moved on, and one houses a Pizza Express, the other a Tesco Direct. The obviously-built-to-be-a-Post-Office building, too, is now a restaurant, and the post office itself now occupies a corner of a newsagent’s. They ilustrate a point David Birch has frequently made: there is nothing permanent about our financial arrangements. Banking itself is only a few hundred years old.

Writing with Victoria Richardson, in their new book Money in the Metaverse: Birch argues this point anew. At one time paper notes seemed as shocking and absurd as cryptocurrencies and non-fungible tokens do today. The skeptic reads that and wonders if the early days of paper notes were as rife with fraud and hot air as NFTs have been. Is the metaverse even still a thing? It’s all AI hype round here now.

Birch and Richardson, however, believe that increasingly our lives will be lived online – a flight to the “cyburbs”, they call it. In one of their early examples of our future, they suggest it will be good value to pay for a virtual ticket (NFT) to sit next to a friend to listen to a concert in a virtual auditorium. It may be relevant that they were likely writing this during the acute phase of the covid pandemic. By now, most of the people I zoomed with then are back doing things in the real world and are highly resistant to returning to virtual, or even hybrid, meetups.

But exactly how financial services might operate isn’t really their point and would be hard to get right eve if it were. Instead, their goal is to explain various novel financial technologies and tools such as NFTs, wallets, smart contracts, and digital identities and suggest possible strategies for businesses to use them to build services. Some of the underlying ideas have been around for at least a couple of decades: software agents that negotiate on an individual’s behalf, and support for multiple disconnected identities to be used in the different roles in life we all have, for example. Others are services that seem to have little to do with the metaverse, such as paperless air travel, already being implemented, and virtual tours of travel destination, which have been with us in some form since video arrived on the web.

The key question – whether the metaverse will see mass adoption – is not one Birch and Richardson can answer. Certainly, I’m dubious about some of the use cases they propose – such as the idea of gamifying life insurance by offering reduced premiums to those who reach various thresholds of physical activity or healthy living. Insurance is supposed to manage risk by pooling it; their proposal would penalize disability and illness.

A second question occurs: what new kinds of crime will these technologies enable? Just this week, Fortune reported that cashlessness has brought a new level of crime to Sweden. Why should the metaverse be different? This, too, is beyond the scope of Birch’s and Richardson’s work, which is to explain but not to either hype or critique. The overall impression the book leaves, however, is of a too-clean computer-generated landscape or smart city mockup, where the messiness of real life is missing.

Soap dispensers and Skynet

In the TV series Breaking Bad, the weary ex-cop Mike Ehrmantraut tells meth chemist Walter White : “No more half measures.” The last time he took half measures, the woman he was trying to protect was brutally murdered.

Apparently people like to say there are no dead bodies in privacy (although this is easily countered with ex-CIA director General Michael Hayden’s comment, “We kill people based on metadata”). But, as Woody Hartzog told a Senate committee hearing in September 2023, summarizing work he did with Neil Richards and Ryan Durrie, half measures in AI/privacy legislation are still a bad thing.

A discussion at Privacy Law Scholars last week laid out the problems. Half measures don’t work. They don’t prevent societal harms. They don’t prevent AI from being deployed where it shouldn’t be. And they sap the political will to follow up with anything stronger.

In an article for The Brink, Hartzog said, “To bring AI within the rule of law, lawmakers must go beyond half measures to ensure that AI systems and the actors that deploy them are worthy of our trust,”

He goes on to list examples of half measures: transparency, committing to ethical principles, and mitigating bias. Transparency is good, but doesn’t automatically bring accountability. Ethical principles don’t change business models. And bias mitigation to make a technology nominally fairer may simultaneously make it more dangerous. Think facial recognition: debias the system and improve its accuracy for matching the faces of non-male, non-white people, and then it’s used to target those same people with surveillance.

Or, bias mitigation may have nothing to do with the actual problem, an underlying business model, as Arvind Narayanan, author of the forthcoming book AI Snake Oil, pointed out a few days later at an event convened by the Future of Privacy Forum. In his example, the Washington Post reported in 2019 on the case of an algorithm intended to help hospitals predict which patients will benefit from additional medical care. It turned out to favor white patients. But, Narayanan said, the system’s provider responded to the story by saying that the algorithm’s cost model accurately predicted the costs of additional health care – in other words, the algorithm did exactly what the hospital wanted it to do.

“I think hospitals should be forced to use a different model – but that’s not a technical question, it’s politics.”.

Narayanan also called out auditing (another Hartzog half measure). You can, he said, audit a human resources system to expose patterns in which resumes it flags for interviews and which it drops. But no one ever commissions research modeled on the expensive random controlled testing common in medicine that follows up for five years to see if the system actually picks good employees.

Adding confusion is the fact that “AI” isn’t a single thing. Instead, it’s what someone called a “suitcase term” – that is, a container for many different systems built for many different purposes by many different organizations with many different motives. It is absurd to conflate AGI – the artificial general intelligence of science fiction stories and scientists’ dreams that can surpass and kill us all – with pattern-recognizing software that depends on plundering human-created content and the labeling work of millions of low-paid workers

To digress briefly, some of the AI in that suitcase is getting truly goofy. Yum Brands has announced that its restaurants, which include Taco Bell, Pizza Hut, and KFC, will be “AI-first”. Among Yum’s envisioned uses, the company tells Benj Edwards at Ars Technica, are being able to ask an app what temperature to set the oven. I can’t help suspecting that the real eventual use will be data collection and discriminatory pricing. Stuff like this is why Ed Zitron writes postings like The Rot-Com Bubble, which hypothesizes that the reason Internet services are deteriorating is that technology companies have run out of genuinely innovative things to sell us.

That you cannot solve social problems with technology is a long-held truism, but it seems to be especially true of the messy middle of the AI spectrum, the use cases active now that rarely get the same attention as the far ends of that spectrum.

As Neil Richards put it at PLSC, “The way it’s presented now, it’s either existential risk or a soap dispenser that doesn’t work on brown hands when the real problem is the intermediate level of societal change via AI.”

The PLSC discussion included a list of the ways that regulations fail. Underfunded enforcement. Regulations that are pure theater. The wrong measures. The right goal, but weakly drafted legislation. Make the regulation ambiguous, or base it on principles that are too broad. Choose conflicting half-measures – for example, require transparency but add the principle that people should own their own data.

Like Cristina Caffarra a week earlier at CPDP, Hartzog, Richards, and Durrie favor finding remedies that focus on limiting abuses of power. Full measures include outright bans, the right to bring a private cause of action, imposing duties of “loyalty, care, and confidentiality”, and limiting exploitative data practices within these systems. Curbing abuses of power, as he says, is nothing new. The shiny new technology is a distraction.

Or, as Narayanan put it, “Broken AI is appealing to broken institutions.”

Illustrations: Mike (Jonathan Banks) telling Walt (Bryan Cranston) in Breaking Bad (S03e12) “no more half measures”.

Review: More Than a Glitch

More Than a Glitch: Confronting Race, Gender, and Ability Bias in Tech
By Meredith Broussard
MIT Press
ISBN: 978-0-262-04765-4

At the beginning of the 1985 movie Brazil, a family’s life is ruined when a fly gets stuck in a typewriter key so that the wrong man is carted away to prison. It’s a visual play on “computer bug”, so named after a moth got trapped in a computer at Harvard.

Based on her recent book More Than a Glitch, NYU associate professor Meredith Broussard, would call both the fly and the moth a “glitch”. In the movie, the error is catastrophic for Buttle-not-Tuttle and his family, but it’s a single, ephemeral mistake that can be prevented with insecticide and cross-checking. A “bug” is more complex and more significant: it’s “substantial”, “a more serious matter that makes software fail”. It “deserves attention”. It’s the difference between the lone rotten apple in a bushel full of good ones and a barrel that causes all the apples put it in to rot.

This distinction is Broussard’s prelude to her fundamental argument that the lack of fairness in computer systems is persistent, endemic, and structural. In the book, she examines numerous computer systems that are already out in the world causing trouble. After explaining the fundamentals of machine bias, she goes through a variety of sectors and applications to examine failures of fairness in each one. In education, proctoring software penalizes darker-skinned students by failing to identify them accurately, and algorithms used to estimate scores on tests canceled during the pandemic penalized exceptional students from unexpected backgrounds. In health, long-practiced “race correction” that derives from slavery preferences white patients for everything from painkillers to kidney transplants – and gets is embedded into new computer systems built to replicate existing practice. If computer developers don’t understand the way in which the world is prejudiced – and they don’t – how can the systems they create be more neutral than the precursors they replace? Broussard delves inside each system to show why, not just how, it doesn’t work as intended.

In other cases Broussard highlights, part of the problem is rigid inflexibility in back-end systems that need to exchange data. There’s little benefit in having 58 gender options if the underlying database only supports two choices. At a doctor’s office, Broussard is told she can only check one box for race; she prefer to check both “black” and “white” because in medical settings it may affect her treatment. The digital world remains only partially accessible. And, as Broussard discovered when she was diagnosed with breast cancer, even supposed AI successes like reading radiology films are overhyped. This section calls back to her 2018 book, Artificial Unintelligence, which did a good job of both explaining how machine learning and “AI” computer systems work and why a lot of the things the industry says work…really don’t (see also self-driving cars).

Broussard concludes by advocating for public interest technology and a rethink. New technology imitates the world it comes from; computers “predict the status quo”. Making change requires engineering technology so that it performs differently. It’s a tall order, and Broussard knows that. But wasn’t that the whole promise the technology founder made? That they could change the world to empower the rest of us?

Review: A History of Fake Things on the Internet

A History of Fakes on the Internet
By Walter J. Scheirer
Stanford University Press
ISBN 2023017876

One of Agatha Christie’s richest sources of plots was the uncertainty of identity in England’s post-war social disruption. Before then, she tells us, anyone arriving to take up residence in a village brought a letter of introduction; afterwards, old-time residents had to take newcomers at their own valuation. Had she lived into the 21st century, the arriving Internet would have given her whole new levels of uncertainty to play with.

In his recent book A History of Fake Things on the Internet, University of Notre Dame professor Walter J. Scheirer describes creating and detecting online fakes as an ongoing arms race. Where many people project doomishly that we will soon lose the ability to distinguish fakery from reality, Scheirer is more optimistic. “We’ve had functional policies in the past; there is no good reason we can’t have them again,” he concludes, adding that to make this happen we need a better understanding of the media that support the fakes.

I have a lot of sympathy with this view; as I wrote recently, things that fool people when a medium is new are instantly recognizable as fake once they become experienced. We adapt. No one now would be fooled by the images that looked real in the early days of photography. Our perceptions become more sophisticated, and we learn to examine context. Early fakes often work simply because we don’t know yet that such fakes are possible. Once we do know, we exercise much greater caution before believing. Teens who’ve grown up applying filters to the photos and videos they upload to Instagram and TikTok, see images very differently than those of us who grew up with TV and film.

Schierer begins his story with the hacker counterculture that saw computers as a source of subversive opportunities. His own research into media forensics began with Photoshop. At the time, many, especially in the military, worried that nation-states would fake content in order to deceive and manipulate. What they found, in much greater volume, was memes and what Schierer calls “participatory fakery” – that is, the cultural outpouring of fakes for entertainment and self-expression, most of it harmless. Further chapters consider cheat codes in games, the slow conversion of hackers into security practitioners, adversarial algorithms and media forensics, shock-content sites, and generative AI.

Through it all, Schierer remains optimistic that the world we’re moving into “looks pretty good”. Yes, we are discovering hundreds of scientific papers with faked data, faked results, or faked images, but we also have new analysis tools to use to detect them and Retraction Watch to catalogue them. The same new tools that empower malicious people enable many more positive uses for storytelling, collaboration, and communication. Perhaps forgetting that the computer industry relentlessly ignores its own history, he writes that we should learn from the past and react to the present.

The mention of scientific papers raises an issue Schierer seems not to worry about: waste. Every retracted paper represents lost resources – public funding, scientists’ time and effort, and the same multiplied into the future for anyone who attempts to build on that paper. Figuring out how to automate reliable detection of chatbot-generated text does nothing to lessen the vast energy, water, and human resources that go into building and maintaining all those data centers and training models (see also filtering spam). Like Scheirer, I’m largely optimistic about our ability to adapt to a more slippery virtual reality. But the amount of wasted resources is depressing and, given climate change, dangerous.

Alabama never got the bomb

There is this to be said for nuclear weapons: they haven’t scaled. Since 1969, when Tom Lehrer warned about proliferation (“We’ll try to stay serene and calm | When Alabama gets the bomb”), a world of treaties, regulation, and deterrents has helped, but even if it hadn’t, building and updating nuclear weapons remains stubbornly expensive. (That said, the current situation is scary enough.)

The same will not be true of drones, James Patton Rogers explained in a recent talk at Kings College London about his new book, Precision: A History of American Warfare. Already, he says, drones are within reach for non-governmental actors such as Mexican drug cartels. At the BBC, Jonathan Marcus estimated in February 2022 that more than 100 nations and non-state actors already have combat drones and these systems are proliferating rapidly. The brief moment in which the US and Israel had an exclusive edge is already gone; Rogers says Iran and Turkey are “drone powers”. Back to the BBC in 2022: Marcus writes that some terrorist groups had already been able to build attack drone systems using commercial components for a few hundred dollars. Rogers put the number of countries with drone capability in 2023 at 113, plus 65 armed groups. He also called them one of the “greatest threats to state security”, noting the speed and abruptness with which they’ve flipped from being protective and their potential for “assassinations, strikes, saturation attacks”.

Rogers, who calls his book an “intellectual history”, traces the beginnings of precision to the end of the long, muddy, casualty-filled conflict of World War I. Never again: instead, remote attacks on military-industrial targets that limit troops on the ground and loss of life. The arrival of the atomic bomb and Russia’s development of same changed focus to the Dr Strangelove-style desire for the technology to mount massive retaliation. John F. Kennedy successfully campaigned on the missile gap. (In this part of Rogers’ presentation, it was impossible not to imagine how effective this amount of energy could have been if directed toward climate change…)

The 1990s and the Gulf War brought a revival of precision in the form of the first cruise missiles and the first drones. But as long ago as 1988 there were warnings that the US could not monopolize drones and they would become a threat. “We need an international accord to control drone proliferation,” Rogers said.

But the threat to state security was not Rogers’ answer when an audience member asked him, “What keeps you awake at night?”

“Drone mass killings targeting ethnic diasporas in cities.”

Authoritarian governments have long reached out to control opposition outside their borders. In 1974, I rented an apartment from the Greek owner of a local highly-regarded restaurant. A day later, a friend reacted in horror: didn’t I know that restaurateur was persona-non-patronize because he had reported Greek student protesters in Ithaca, New York to the military junta then in power and there had been consequences for their families back home? No, I did not.

As an informant, landlord’s powers were limited, however. He could go to and photograph protests; if he couldn’t identify the students he could still send their pictures. But he couldn’t amass comprehensive location data tracking their daily lives, operate a facial recognition system, or monitor them on social media and infer their social graphs. A modern authoritarian government equipped with Internet connections can do all of that and more, and the data it can’t gather itself it can obtain by purchase, contract, theft, hacking, or compulsion.

In Canada, opponents of Chinese Communist Party policies report harassment and intimidation. Freedom House reports that China’s transnational repression also includes spyware, digital threats, physical assault, and cooption of other countries, all escalating since 2014. There’s no reason for this sort of thing to be limited to the Chinese (and Russians); Citizen Lab has myriad examples of governments’ use of spyware to target journalists, political opponents, and activists, inside or outside the countries where they’re active.

Today, even in democratic countries there is an ongoing trend toward increased and more militaristic surveillance of migrants and borders. In 2021, Statewatch reported on the militarization of the EU’s borders along the Mediterranean, including a collaboration between Airbus and two Israeli companies to use drones to intercept migrant vessels Another workshop that same year made plain the way migrants are being dataveilled by both governments and the aid agencies they rely on for help. In 2022, the courts ordered the UK government to stop seizing the smartphones belonging to migrants arriving in small boats.

Most people remain unaware of this unless some poliitician boasts about it as part of a tough-on-immigration platform. In general, rights for any kind of foreigners – immigrants, ethnic minorities – are a hard sell, if only because non-citizens have no vote, and an even harder one against the headwind of “they are not us” rhetoric. Threats of the kind Rogers imagined are not the sort nations are in the habit of protecting against.

It isn’t much of a stretch to imagine all those invasive technologies being harnessed to build a detailed map of particular communities. From there, given affordable drones, you just need to develop enough malevolence to want to kill them off, and be the sort of country that doesn’t care if the rest of the world despises you for it.

Illustrations: British migrants to Australia in 1949 (via Wikimedia).

Review: The Bill Gates Problem

The Bill Gates Problem: Reckoning with the Myth of the Good Billionaire
By Tim Schwab
Metropolitan Books
ISBN: 978-1-25085009-6

Thirty years ago, the Federal Trade Commission began investigating one of the world’s largest technology companies on antitrust grounds. Was it leveraging its monopoly in one area to build dominance in others? Did it bully smaller competitors into disclosing their secrets, which it then copied? And so on. That company was Microsoft, Windows was giving it leverage over office productivity software, web browsers, and media players, and its leader was Bill Gates. In 1999, the courts ruled Microsoft a monopoly.

At the time, it was relatively commonplace for people to complain that Gates was insufficiently charitable. Why wasn’t he more philanthropic, given his vast and increasing wealth? (Our standards for billionaire wealth were lower back then.) Be careful what you wish for…

The transition from monopolist mogul to beneficent social entrepreneur where Tim Schwab starts in The Bill Gates Problem: Reckoning with the Myth of the Good Billionaire. In Schwab’s view, the reason is well-executed PR, in which category he includes the many donations the foundation makes to journalism organizations.

I have heard complaints for years that the Bill and Melinda Gates Foundation’s approach to philanthropy favors expensive technological interventions over cheaper, well-established ones. In education that might mean laptops and edtech software rather than training teachers; in medicine that might mean vaccine research rather than clean water. Schwab’s investigative work turns up dozens such stories in the areas BMGF works in: family planning, education, health. Yet, Schwab writes, citing numerous sources for his figures, for all the billions BMGF has poured into these areas, it has failed to meet its stated objectives.

You can argue that case, but Schwab moves on from there to examine the damaging effects of depending on a billionaire, no matter how smart and well-intentioned, to finance services that might more properly be the business of the state. No one elected Gates, and no one has voted on the priorities he has chosen to set. The covid pandemic provides a particularly good example. One of the biggest concerns as efforts to produce vaccines got underway was ensuring that access would not be limited to rich countries. Many believed that the most efficient way of doing this was to refrain from patenting the vaccines, and help poorer countries build their own production facilities. Gates was one of those who opposed this approach, arguing that patents were necessary to reward pharmaceutical companies for the investment they poured into research, and also that few countries had the expertise to make the vaccines. Gates gave in to pressure and reversed his position in May 2021 to support a “narrow waiver”. Reading that BMGF is the biggest funder of the WHO and remembering his preference for technological interventions made me wonder: how much do we have Gates to thank for the emphasis on vaccines and the reluctance to push cheaper non-pharmaceutical interventions like masks, HEPA filters, and ventilation in countries like the UK?

Schwab goes into plenty of detail about all this. But his wider point is to lay out the power Gates’s massive wealth – both the foundation’s and his own – gives him over the charitable sector and, through public-partnerships, many of the nations in which he operates. Schwab also calls Gates’s approach “philanthropic colonialism” because the bulk of his donations go to organizations based in the West, rather than directly to their counterparts elsewhere.

Pointing out the amount of taxpayer subsidy the foundation gets through the tax exemptions charities get, Schwab asks if we’re really getting value for our money. Wouldn’t we be better off collecting taxes and setting our own agendas? Is there really any such thing as a “good” billionaire?