net.wars

Blur

In 2013, London’s Royal Court Theatre mounted a production of Jennifer Haley’s play The Nether. (Spoiler alert!) In its story of the relationship between an older man and a young girl in a hidden online space, nothing is as it seems…

At last week’s Gikii, Anna-Maria Piskopani and Pavlos Panagiotidis invoked the play to ask whether, given that virtual crimes can create real harm, can virtual worlds help people safely experience the worst parts of themselves without legitimizing them in the real world?

Gikii papers mix technology, law, and pop culture into thought experiments. This year’s official theme was “Technology in its Villain Era?”

Certainly some presentations fit this theme. Paweł Urzenitzok, for example, warned of laws that seem protective but enable surveillance, while varying legal regimes enable arbitrage as companies shop for the most favorable forum. Julia Krämer explored the dark side of app stores, which are getting 30% commissions on a flood of “AI boyfriends” and “perfect wives”. (Not always perfect; users complain that some of them “talk too much”.)

Andelka Phillips warned of the uncertain future risks of handing over personal data highlighted by the recent sale of 23andMe to its founder, Anne Wojcicki. Once the company filed for bankruptcy protection, the class action suits brought against it over the 2023 data breach were put on hold. The sale, she said, ignored concerns raised by the privacy ombudsman. And, Leila Debiasi said, your personal data can be used for AI training after you die.

In another paper, Peter van de Waerdt and Gerard Ritsema van Eck used Doctor Who’s Silents, who disappear from memory when people turn away, to argue that more attention should be paid to enforcing EU laws requiring data portability. What if, for example, consumers could take their Internet of Things device and move it to a different company’s service? Also in that vein was Tim van Zuijlen, who suggested consumers assemble to demand their collective rights to fight back against planned obsolescence. This is already happening; in multiple countries consumers are suing Apple over slowed-down iPhones.

The theme that seemed to emerge most clearly, however, is our increasingly blurred lines, with AI as a prime catalyst. In the before-generative-AI times, The Nether blurred the line between virtual and real. Now, Hedye Tayebi Jazayeri and Mariana Castillo-Hermosilla found gamification in real life – are credit scores so different from game scores? Dongshu Zhou asked if you can ever really “delete yourself” after a meme about you has gone viral and you have become “digital folklore”. In another, Lior Weinstein suggested a “right to be nonexistent” – that is, invisible to the institutions and systems that seprately Kimberly Paradis said increasingly want us all to be legible to them.

For Joanne Wong, real brainrot is a result of the AI-fueled spread of “low-quality” content such as the burst of remixes and parodies of Chinese home designer Little John. At AI-fueled hyperspeed, copyright become irrelevant.

Linnet Taylor and Tjaša Petročnik tested chatbots as therapists, finding that they give confused and conflicting responses. Ask what regulations govern them, and they may say at once that they are not therapists *and* that they are certified by their state’s authority. At least one resisted being challenged: “What are you, a cop or something?”. That’s probably the most human-like response one of these things has ever delivered – but it’s still not sentient. It’s just been programmed that way.

Gikii’s particular blend of technology, law, and pop culture always has its surreal side (see last year), as participants attempt to navigate possible futures. This year, it struggled to keep up with the weirdness of real life. In Albania, the government has appointed a chatbot, Diella as a minister, intending it to cut corruption in procurement. Diella will sit in the cabinet, albeit virtually, and be used to assess the merit of private companies’ responses to public tenders. Kimberly Breedon used this example to point out the conflict of interest inherent in technology companies providing tools to assess – in some cases – themselves. Breedon’s main point was important, given that we are already seeing AI used to speed up and amplify crime. Although everyone talks about using AI to cut corruption, no one is talking about how AI might be used *for* corruption. Asked how that would work, she noted the potential for choosing unrepresentative data or screening out disfavored competitors.

In looking up that Albanian AI minister, I find that the UK has partnered with Microsoft to create a package of AI tools intended to speed up the work of the civil service. Naturally it’s called Humphrey. MPs are at it, too, experimenting with using AI to write their Parliamentary speeches.

All of this is why Syamsuriatina Binti Ishak argued what could be Gikii’s mission statement: we must learn from science fiction and the”what-ifs” it offers to allow us to think our fears through so that “if the worst happens we know how to live in that universe”. Would we have done better as covid arrived if we paid more attention to the extensive universe of pandemic fiction? Possibly not. As science fiction writer Charlie Stross pointed out at the time, none of those books imagined governments as bumbling as many proved to be.

Illustrations: “Diella”, Albania’s procurement minister chatbot.

Wendy M. Grossman is an award-winning journalist. 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 or Bluesky.

Dethroned

This is a version of a paper that Jon Crowcroft and I delivered at this week’s gikii conference.

She sounded shocked. But also: as though the word she had to pronounce in front of the world’s press was one she had never encountered before and needed to take care to get right. Stan-o-zo-lol. It was 1988, and the Canadian sprinter Ben Johnson had tested positive for it. It was two days, after he had won the gold medal in the 100m men’s race at the Seoul Olympics.

In the years since, that race has become known as the dirtiest race in history. Of the top eight finishers, just one has never been caught doping: US runner Calvin Smith, who was awarded the bronze medal after Johnson was disqualified.

Doping controls were in their infancy then. As athletes and their coaches and doctors moved on from steroids to EPO and human growth hormone, anti-doping scientists, always trailing behind, developed new tests. Recognizing that in-competition testing didn’t catch athletes during training, when doping regimens are most useful, the authorities began testing outside of competiton, which in 2004 in turn spawned the “whereabouts” system athletes must use to tell testers where they’re going to be for one hour of every day. Athlete biological passports came into use in 2008 to track blood markers over time and monitor for suspicious changes brought by drugs yet to have tests.

The plan was for the 2012 London Olympics to be the cleanest ever staged. Scientists built a lab; they showed off new techniques to the press. Afterwards, they took bows. In a report published in October 2012, independent observers wrote, the organizers “successfully implemented measures to protect the rights of clean athletes”. The report found only eight out of more than 5,000 samples tested positive during the games. Success?

It is against this background that in 2014 the German TV channel MDR, whose journalist Hajo Seppelt specializes in doping investigations, aired the Icarus, Grigory Rodchenkov, former director of Moscow’s doping control lab, spilled the story of swapped samples and covered-up tests. And 2012? Rodchenkov called it the dirtiest Olympics in history. The UK’s anti-doping lab, he said, missed 126 positive tests.

In April, Esther Addley reported in the Guardian that “the dirtiest race in history” has a new contender: the women’s 1500 meter race at the 2012 London Olympics.

In the runup to 2012, the World Anti-Doping Agency decided to check their work. They arranged to keep athletes’ samples, frozen, for eight years so they could be rested later as dope-testing science improved and expanded. In 2016, reanalysis of 265 samples across five sports from athletes who might participate in the 2016 Rio games found banned substances in samples relating to 23 athletes.

That turned out to be only the beginning. In the years since, athlete after athlete in that race have had their historical results overturned as a result of abnormalities in their biological passports. Just last year – 2024! – one more athlete was disqualified from that race after her frozen sample tested positive for steroids.

The official medal list now awards gold to Maryam Yusuf Jamal (originally the bronze medalist); silver to Abeba Aregawi (upgraded from fifth place to bronze, and then to silver); and bronze to Shannon Rowbury, the sixth-place finisher. Is retroactive fairness possible?

In our gikii paper, Jon Crowcroft and I think not. The original medalists have lost their places in the rolls of honor, but they’ve had a varying number of years to exploit their results while they stood. They got the medal ceremony while in the flush of triumph, the national kudos, and the financial and personal opportunities that go with it.

In addition, Crowcroft emphasizes that runners strategize. You run a race very differently depending on who your competitors are and what you know about how they run. Jamal, Aragawi, and Rowbury would have faced a very different opposition both before and during the final had the anti-doping system worked as it was supposed to, with unpredictable results.

The anti-doping system is essentially a security system, intended to permit some behaviors and elminate others. Many points of failure are obvious simply from analyzing misplaced incentives. some substances can’t be detected, which WADA recognizes by barring methods as well as substances. Some that can be are overlooked – see, for example, meldonium, which was used by hundreds of Eastern European athletes for a decade or more before WADA banned it. More, it is fundamentally unfair to look at athletes as independent agents of their own destinies. They are the linchpins of ecosystems that include coaches trainers, doctors, nutritionists, family members, agents, managers, sponsors, and national and international sporting bodies.

In a 2006 article, Bruce Schneier muses on a different unfairness: that years later athletes have less ability to contest findings, as they can’t be retested. That’s partly true. In many cases, athletes can’t be retested even a day later. Instead, their samples are divided into two. The “B”sample is tested for confirmation if the “A” sample produces an adverse analytical finding.

If you want to ban doping, or find out who was using what and when, retrospective testing is a valuable tool. It can certainly bring a measure of peace and satisfaction to the athletes who felt cheated. But it doesn’t bring fairness.

Illustrations: The three top finishers on the day of the women’s 1500 meter race at the 2012 Olympics; on the right is Maryam Yusuf Jamal, later promoted to gold medal.

Beware the duck

Once upon a time, “convergence” was a buzzword. That was back in the days when audio was on stereo systems, television was on a TV, and “communications” happened on phones that weren’t computers. The word has disappeared back into its former usage pattern, but it could easily be revived to describe what’s happening to content as humans dive into using generative tools.

Said another way. Roughly this time last year, the annual technology/law/pop culture conference Gikii was awash in (generative) AI. That bubble is deflating, but in the experiments that nonetheless continue a new topic more worthy of attention is emerging: artificial content. It’s striking because what happens at this gathering, which mines all types of popular culture for cues for serious ideas, is often a good guide to what’s coming next in futurelaw.

That no one dared guess which of Zachary Cooper‘s pair of near-identicalaudio clips was AI-generated, and which human-performed was only a starting point. One had more static? Cooper’s main point: “If you can’t tell which clip is real, then you can’t decide which one gets copyright.” Right, because only human creations are eligible (although fake bands can still scam Spotify).

Cooper’s brief, wild tour of the “generative music underground” included using AI tools to create songs whose content is at odds with their genre, whole generated albums built by a human producer making thousands of tiny choices, and the new genre “gencore”, which exploits the qualities of generated sound (Cher and Autotune on steroids). Voice cloning, instrument cloning, audio production plugins, “throw in a bass and some drums”….

Ultimately, Cooper said, “The use of generative AI reveals nothing about the creative relationship to work; it destabilizes the international market by having different authorship thresholds; and there’s no means of auditing any of it.” Instead of uselessly trying to enforce different rights predicated on the use or non-use of a specific set of technologies, he said, we should tackle directly the challenges new modes of production pose to copyright. Precursor: the battles over sampling.

Soon afterwards, Michael Veale was showing us Civitai, an Idaho-based site offering open source generative AI tools, including fine-tuned models. “Civitai exists to democratize AI media creation,” the site explains. “Everything has a valid legal purpose,” Veale said, but the way capabilities can be retrained and chained together to create composites makes it hard to tell which tools, if any, should be taken down, even for creators (see also the puzzlement as Redditors try to work this out). Even environmental regulation can’t help, as one attendee suggested: unlike large language models, these smaller, fine-tuned models (as Jon Crowcroft and I surmised last year would be the future) are efficient; they can run on a phone.

Even without adding artificial content there is always an inherent conflict when digital meets an analog spectrum. This is why, Andy Phippen said, the threshold of 18 for buying alcohol and cigarettes turns into a real threshold of 25 at retail checkouts. Both software and humans fail at determining over-or-under-18, and retailers fear liability. Online age verification as promoted in the Online Safety Act will not work.

If these blurred lines strain the limits of current legal approaches, others expose gaps in the law. Andrea Matwyshyn, for example, has been studying parallels I’ve also noticed between early 20th century company towns and today’s tech behemoths’ anti-union, surveillance-happy working practices. As a result, she believes that regulatory authorities need to start considering closely the impact of data aggregation when companies merge and look for company town-like dynamics”.

Andelka Phillips parodied the overreach of app contracts by imagining the EULA attached to “ThoughtReader app”. A sample clause: “ThoughtReader may turn on its service at any time. By accepting this agreement, you are deemed to accept all monitoring of your thoughts.” Well, OK, then. (I also had a go at this here, 19 years ago.)

Emily Roach toured the history of fan fiction and the law to end up at Archive of Our Own, a “fan-created, fan-run, nonprofit, noncommercial archive for transformative fanworks, like fanfiction, fanart, fan videos, and podfic”, the idea being to ensure that the work fans pour their hearts into has a permanent home where it can’t be arbitrarily deleted by corporate owners. The rules are strict: not so much as a “buy me a coffee” tip link that could lead to a court-acceptable claim of commercial use.

History, the science fiction writer Charles Stross has said, is the science fiction writer’s secret weapon. Also at Gikii: Miranda Mowbray unearthed the 18th century “Digesting Duck” automaton built by Jacques de Vauconson. It was a marvel that appeared to ingest grain and defecate waste and that in its day inspired much speculation about the boundaries between real and mechanical life. Like the amazing ancient Greek automata before it, it was, of course, a purely mechanical fake – it stored the grain in a small compartment and released pellets from a different compartment – but today’s humans confused into thinking that sentences mean sentience could relate.

Illustrations: One onlooker’s rendering of his (incorrect) idea of the interior of Jacques de Vaucanson’s Digesting Duck (via Wikimedia).

Doom cyberfuture

Midway through this year’s gikii miniconference for pop culture-obsessed Internet lawyers, Jordan Hatcher proposed that generational differences are the key to understanding the huge gap between the Internet pioneers, who saw regulation as the enemy, and the current generation, who are generally pushing for it. While this is a bit too pat – it’s easy to think of Millennial libertarians and I’ve never thought of Boomers as against regulation, just, rationally, against bad Internet law that sticks – it’s an intriguing idea.

Hatcher, because this is gikii and no idea can be presented without a science fiction tie-in, illustrated this with 1990s movies, which spread the “DCF-84 virus” – that is, “doom cyberfuture-84”. The “84” is not chosen for Orwell but for the year William Gibson’s Neuromancer was published. Boomers – he mentioned John Perry Barlow, born 1947, and Lawrence Lessig, born 1961 – were instead infected with the “optimism virus”.

It’s not clear which 1960s movies might have seeded us with that optimism. You could certainly make the case that 1968’s 2001: A Space Odyssey ends on a hopeful note (despite an evil intelligence out to kill humans along the way), but you don’t even have to pick a different director to find dystopia: I see your 2001 and give you Dr Strangelove (1964). Even Woodstock (1970) is partly dystopian; the consciousness of the Vietnam war permeates every rain-soaked frame. But so did the belief that peace could win: so, wash.

For younger people’s pessimism, Hatcher cited 1995’s Johnny Mnemonic (based on a Gibson short story) and Strange Days.

I tend to think that if 1990s people are more doom-laden than 1960s people it has more to do with real life. Boomers were born in a time of economic expansion, relatively affordable education and housing, and and when they protested a war the government eventually listened. Millennials were born in a time when housing and education meant a lifetime of debt, and when millions of them protested a war they were ignored.

In any case, Hatcher is right about the stratification of demographic age groups. This is particularly noticeable in social media use; you can often date people’s arrival on the Internet by which communications medium they prefer. Over dinner, I commented on the nuisance of typing on a phone versus a real keyboard, and two younger people laughed at me: so much easier to type on a phone! They were among the crowd whose papers studied influencers on TikTok (Taylor Annabell, Thijs Kelder, Jacob van de Kerkhof, Haoyang Gui, and Catalina Goanta) and the privacy dangers of dating apps (Tima Otu Anwana and Paul Eberstaller), the kinds of subjects I rarely engage with because I am a creature of text, like most journalists. Email and the web feel like my native homes in a way that apps, game worlds, and video services never will. That dates me both chronologically and by my first experiences of the online world (1991).

Most years at this event there’s a new show or movie that fires many people’s imagination. Last year it was Upload with a dash of Severance. This year, real technological development overwhelmed fiction, and the star of the show was generative AI and large language models. Besides my paper with Jon Crowcrosft, there was one from Marvin van Bekkum, Tim de Jonge, and Frederik Zuiderveen Borgesius that compared the science fiction risks of AI – Skynet, Roko’s basilisk, and an ordering of Asimov’s Laws that puts obeying orders above not harming humans (see XKCD, above) – to the very real risks of the “AI” we have: privacy, discrimination, and environmental damage.

Other AI papers included one by Colin Gavaghan, who asked if it actually matters if you can’t tell whether the entity that’s communicating with you is an AI? Is that what you really need to know? You can see his point: if you’re being scammed, the fact of the scam matters more than the nature of the perpetrator, though your feelings about it may be quite different.

A standard explanation of what put the “science” in science fiction (or the “speculative” in “speculative fiction”) used be to that the authors ask, “What if?” What if a planet had six suns whose interplay meant that darkness only came once every 1,000 years? Would the reaction really be as Ralph Waldo Emerson imagined it? (Isaac Asimov’s Nightfall). What if a new link added to the increasingly complex Boston MTA accidentally turned the system into a Mobius strip (A Subway Named Mobius, by Armin Joseph Deutsch). And so on.

In that sense, gikii is often speculative law, thought experiments that tease out new perspectives. What if Prime Day becomes a culturally embedded religious holiday (Megan Rae Blakely)? What if the EU’s trademark system applied in the Star Trek universe (Simon Sellers)? What if, as in Max Gladsone’s Craft Sequence books, law is practical magic (Antonia Waltermann)? In the trademark example, time travel is a problem; as competing interests can travel further and further back to get the first registration. In the latter…well, I’m intrigued by the idea that a law making dumping sewage in England’s rivers illegal could physically stop it from happening without all the pesky apparatus of law enforcement and parliamentary hearings.

Waltermann concluded by suggesting that to some extent law *is* magic in our world, too. A useful reminder: be careful what law you wish for because you just may get it. Boomer!

Illustrations: Part of XKCD‘s analysis of Asimov’s Laws of Robotics.

Small data

Shortly before this gets posted, Jon Crowcroft and I will have presented this year’s offering at Gikii, the weird little conference that crosses law, media, technology, and pop culture. This is what we will possibly may have said, as I understand it, with some added explanation for the slightly less technical audience I imagine will read this.

Two years ago, a team of four researchers – Timnit Gebru, Emily Bender, Margaret Mitchell (writing as Shmargaret Shmitchell), and Angelina McMillan-Major – wrote a now-famous paper called On the Dangers of Stochastic Parrots (PDF) calling into question the usefulness of the large language models (LLMs) that have caused so much ruckus this year. The “Stochastic Four” argued instead of small models built on carefully curated data: less prone to error, less exploitive of people’s data, less damaging to the planet. Gebru got fired over this paper; Google also fired Mitchell soon afterwards. Two years later, neural networks pioneer Geoff Hinton quit Google in order to voice similar concerns.

Despite the hype, LLMs have many problems. They are fundamentally an extractive technology and are resource-intensive. Building LLMs requires massive amounts of training data; so far, the companies have been unwilling to acknowledge their sources, perhaps because (as is happening already) they fear copyright suits.

More important from a technical standpoint, is the issue of model collapse; that is, models degrade when they begin to ingest synthetic AI-generated data instead of human input. We’ve seen this before with Google Flu Trends, which degraded rapidly as incoming new search data included many searches on flu-like symptoms that weren’t actually flu, and others that simply reflected the frequency of local news coverage. “Data pollution” as LLM-generated data fills the web, will mean that the web will be an increasingly useless source of training data for future generations of generative AI. Lots more noise, drowning out the signal (in the photo above, the signal would be the parrot).

Instead, if we follow the lead of the Stochastic Four, the more productive approach is small data – small, carefully curated datasets that train models to match specific goals. Far less resource-intensive, far fewer issues with copyright, appropriation, and extraction.

We know what the LLM future looks like in outline: big, centralized services, because no one else will be able to amass enough data. In that future, surveillance capitalism is an essential part of data gathering. SLM futures could look quite different: decentralized, with realigned incentives. At one point, we wanted to suggest that small data could bring the end of surveillance capitalism; that’s probably an overstatement. But small data could certainly create the ecosystem in which the case for mass data collection would be less compelling.

Jon and I imagined four primary alternative futures: federation, personalization, some combination of those two, and paradigm shift.

Precursors to a federated small data future already exist; these include customer service chatbots, predictive text assistants. In this future, we could imagine personalized LLM servers designed to serve specific needs.

An individualized future might look something like I suggested here in March: a model that fits in your pocket that is constantly updated with material of your own choosing. Such a device might be the closest yet to Vannevar Bush’s 1945 idea of the Memex (PDF), updated for the modern era by automating the dozens of secretary-curators he imagined doing the grunt work of labeling and selection. That future again has precursors in techniques for sharing the computation but not the data, a design we see proposed for health care, where the data is too sensitive to share unless there’s a significant public interest (as in pandemics or very rare illnesses), or in other data analysis designs intended to protect privacy.

In 2007, the science fiction writer Charles Stross suggested something like this, though he imagined it as a comprehensive life log, which he described as a “google for real life”. So this alternative future would look something like Stross’s pocket $10 life log with enhanced statistics-based data analytics.

Imagining what a paradigm shift might look like is much harder. That’s the kind of thing science fiction writers do; it’s 16 years since Stross gave that life log talk. However, in his 2018 history of advertising, The Attention Merchants, Columbia professor Tim Wu argued that industrialization was the vector that made advertising and its grab for our attention part of commerce. A hundred and fifty-odd years later, the centralizing effects of industrialization are being challenged starting with energy via renewables and local power generation and social media via the fediverse. Might language models also play their part in bringing a new, more collaborative and cooperative society?

It is, in other words, just possible that the hot new technology of 2023 is simply a dead end bringing little real change. It’s happened before. There have been, as Wu recounts, counter-moves and movements before, but they didn’t have the technological affordances of our era.

In the Q&A that followed, Miranda Mowbray pointed out that companies are trying to implement the individualized model, but that it’s impossible to do unless there are standardized data formats, and even then hard to do at scale.

Illustrations: Spot the parrot seen in a neighbor’s tree.

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 Author Wendy M. GrossmanPosted on September 8, 2023September 8, 2023Categories AI, Events, New tech, old knowledgeTags gikii4 Comments on Small data