New APPS: Art, Politics, Philosophy, Science

Another Platonism in LLM Debates, Part 2. The Wittgensteinian strategy

Last time, I started a look at the work of the early AI researcher Margaret Masterson of the Cambridge Language Research Unit (CLRU).  As demonstrated by Lydia Liu in a pair of articles (here and here), Masterson proceeded from Wittgenstein to a thorough deconstruction of traditional ideas of word meaning, moving instead to treating meaning as a function of a word’s associations, as we might find in a thesaurus.  This approach is a clear forerunner to the distributed view of language applied in current LLMs.  Here I’ll outline the basics of the Masterman approach and show how it applies to LLMs.

Masterson’s starting point is a Wittgensteinian point about the distinction between a word and a pattern. Counting with words would be “one, two three.”  Counting with patterns would be “-, --, ---.”  But what if we counted “one, one one, one one one.”  Can words function as patterns?  Masterman applies the thought to the classical Chinese character “zi” (字, which I’ll write here as “zi”), the meaning of which depends on its context and placement in a given text.  Thus, “for Masterman, the zi is what makes the general and abstract category of the written sign possible, for not only does the zi override the Wittgensteinian distinction of word and pattern, but it also renders the distinction of word and nonword superfluous” (Witt., 442).

AI and Copyright: Training Data and Transformative Fair Use

I’ve been loosely tracking the AI and copyright cases, most notably the Thaler litigation, where Thaler keeps losing the argument that work solely by an AI should get copyright protection.  To summarize: everybody who has ruled on that said that only work involving humans can get copyright protection.  As I said at the time, I think a good policy reason in support of this argument is that if pure AI could get copyright, it could produce millions of copyrighted images in almost zero time.  That’s got nothing to do with incentivizing human creation.  It was easy to miss given the deluge of atrocious Supreme Court decisions, but last week, a pair of district court judges ruled on a different (but not unrelated, in terms of markets) AI copyright question – whether scraping online text for training data is fair use.  Both cases are in the Northern District of California, so we can expect the 9^th Circuit to have the first appellate decision on this topic.

By way of background: fair use is an affirmative defense against copyright infringement.  That means that if you accuse me of infringement, I can defend myself as having engaged in “fair use,” which basically means “use that the copyright owner doesn’t like, but that we as a society think should be allowed for policy reasons.”  It could also mean “use that everybody thinks is ok, but for which licensing would be so inefficient that a licensing market would never emerge.”  Fair use is supposed to be decided case-by-case.  It depends on four factors: the “nature and purpose” of the (allegedly infringing) use, the nature of the copyrighted work, the amount of it used, and the market effects of the infringing use.  The middle two factors tend not to matter much.  The first factor is usually decided by a deciding whether the use in question is “transformative.”  For example, consider parody; the Supreme Court ruled back in 1994 that a 2 Live Crew parody of Roy Orbison’s “Pretty Woman.” was fair use.  The most closely analogous case I know to the training data was an appellate decision about Google thumbnails.

Another Platonism in LLM Debates, Part 1: A Wittgensteinian Rejection of Cartesian AI

By Gordon Hull

There’s an emerging literature on Large Language Models (LLMs, like ChatGPT) that basically argues that they undermine a bunch of our existing assumptions about how language works.  As I argued in a paper a year and a half ago, there’s an underlying Cartesianism in a lot of our reflections on AI, which relies on a mind/body distinction (people have minds, other things don’t), and then takes language use as sufficient evidence that one’s interlocutor has a mind.  As I argued there, part of what makes LLMs so alarming is that they clearly do not possess a mind, but they do use language.  So they’re the first examples we have of an artifact that can use language; language-use is no longer sufficient to indicate mindedness.  In that paper, drew the implication that we need to abandon our Cartesianism about AI (caring whether it “has a mind”) and become more Hobbesian (thinking about the sociopolitical and regulatory implications of language-producing artifacts).  Treating LLMs as the origin points of speech has real risks, including making the human labor that produces them invisible, and making it harder to impose liability since machines can’t meet a standard scienter requirement for assigning tort liability.

Here I want to take up a somewhat different thread, one that I started exploring a while ago under the general topic of iterability in language models.  This thread takes the literature on language models seriously; where I want to go with it is to talk about an under-discussed latent Platonism in how we tend to approach language(models).   I’ll start with the literature, which divides into a couple of sections, a Wittgensteinian and a Derridean.

1. The Wittgensteinian Rejection of Cartesian AI

Lydia Liu makes the case for a direct Wittgensteinian influence on the development of ML, via the Cambridge Researcher Margaret Masterson.  I only ran into this work recently, so on the somewhat hubristic assumption that other folks in philosophy also don’t know it, I’ll offer a basic summary here (in my defense: Liu says that “the news of AI researchers’ longtime engagement with Wittgenstein has been slow to arrive.”  She then adds that “the truth is that Wittgenstein’s philosophy of language is so closely bound up with the semantic networks of the computer from the mid-1950s down to the present that we can no longer turn a blind eye to its embodiment in the AI machine” (Witt., 427)).

Tidbit of good news on indirect costs

The NSF had attempted to reduce indirect costs (F&A) on all future grants to 15%, in a somewhat more coherent version of the NIH's effort to do so for all ongoing and future grants.   A federal court today enjoined the rate cut, vacating the new rule, finding that "National Science Foundation’s 15% Indirect Cost Rate and the Policy Notice: Implementation of Standard 15% Indirect Cost Rate, NSF 25-034 are invalid, arbitrary and capricious, and contrary to law"

 

Helen de Cruz, 1978-2025

Former NewAPPS blogger Helen de Cruz passed away yesterday, Friday, June 20, 2025.  I never met Helen personally, but they were part of a vibrant community of bloggers at NewAPPS that welcomed me and provided the supportive context for my own development as a blogger ten years ago.  That experience was, and is, very important to me, and I will always be grateful for it.

I recommend Eric Schliesser's remarks here.

Helen's NewApps writings are here. 

Your Brain on ChatGPT

I wish I’d come up with that title, but it actually belongs to a new study led by Natalia Kosmyna of the MIT Media Labs.  The study integrates brain imaging with questions and behavioral data to explore what happens when people write essays using large language models (LLMs) like ChatGPT.  I haven’t absorbed it all yet – and some of the parts on brain imaging are well beyond my capacity to assess – but the gist of it is to confirm what one might suspect, that writing essays with ChatGPT isn’t really very good exercise for your brain.  The study assigned participants to one of three groups and had each write essays. One got to use an LLM, one used Google search, and one wasn’t allowed to use either.

The results weren’t a total surprise:

“Taken together, the behavioral data revealed that higher levels of neural connectivity and internal content generation in the Brain-only group correlated with stronger memory, greater semantic accuracy, and firmer ownership of written work. Brain-only group, though under greater cognitive load, demonstrated deeper learning outcomes and stronger identity with their output. The Search Engine group displayed moderate internalization, likely balancing effort with outcome. The LLM group, while benefiting from tool efficiency, showed weaker memory traces, reduced self-monitoring, and fragmented authorship. This trade-off highlights an important educational concern: AI tools, while valuable for supporting performance, may unintentionally hinder deep cognitive processing, retention, and authentic engagement with written material. If users rely heavily on AI tools, they may achieve superficial fluency but fail to internalize the knowledge or feel a sense of ownership over it” (138)

These results were corroborated by the brain imaging, and “brain connectivity systematically scaled down with the amount of external support: the Brain‑only group exhibited the strongest, widest‑ranging networks, Search Engine group showed intermediate engagement, and LLM assistance elicited the weakest overall coupling” (2 (from the abstract)).   That is:

New Paper: "AI and Healthcare Disparities: Lessons from a Cautionary Tale in Knee Radiology"

This paper, which has been forthcoming in Journal of Medicine and Philosophy for a while, is my foray into AI and healthcare, particularly medical imaging.  It synthesizes some of what I have to say about structural injustice in AI use (and why "bias" isn't the right way to assess it), and uses a really interesting case study from the literature to explore why it's important to understand AI as part of sociotechincal systems - and how understanding it as part of sociotechnical systems makes a big difference in seeing when/how it can be helpful (or not).  Here's the abstract:

Enthusiasm about the use of AI in medicine has been tempered by concern that algorithmic systems can be unfairly biased against racially minoritized populations. This paper uses work on racial disparities in knee osteoarthritis diagnoses to underline that achieving justice in the use of AI in medical imaging will require attention to the entire sociotechnical system within which it operates, rather than isolated properties of algorithms. Using AI to make current diagnostic procedures more efficient risks entrenching existing disparities; a recent algorithm points to some of the problems in current procedures while highlighting systemic normative issues that need to be addressed while designing further AI systems. The paper thus contributes to a literature arguing that bias and fairness issues in AI be considered as aspects of structural inequality and injustice and to highlighting ways that AI can be helpful in making progress on these.

Bullshit AI Jobs

Last week we heard the latest installment in the prophesized AI jobs apocalypse.  This time, it was Dario Amodi, the CEO of Anthropic, who told Axios that “AI could wipe out half of all entry-level white-collar jobs — and spike unemployment to 10-20% in the next one to five years” (italics original).  Axios adds: “Imagine an agent writing the code to power your technology, or handle finance frameworks and analysis, or customer support, or marketing, or copy editing, or content distribution, or research. The possibilities are endless — and not remotely fantastical. Many of these agents are already operating inside companies, and many more are in fast production …. Make no mistake: We've talked to scores of CEOs at companies of various sizes and across many industries. Every single one of them is working furiously to figure out when and how agents or other AI technology can displace human workers at scale. The second these technologies can operate at a human efficacy level, which could be six months to several years from now, companies will shift from humans to machines.”  The piece then argues that this will be different from previous technological disruptions because of the speed with which it will occur.

Someone should tell that to the workers placed out of work all-but overnight by the development of machinery in the nineteenth century, as detailed by Marx (who helpfully notes in the Machinery chapter in Capital that the drive to full, steam-engine driven automation is motivated by the inability of capitalists to extract any more surplus value from over-exploited workers).  One should also remember, with Jathan Sadowski, that these sorts of proclamations are in part designed to create their own reality, such that “the power of expectations can have a disciplining effect on what people think” and that “the capitalist system is designed to pummel us into submission, preventing us from imagining life could be any other way, let alone allowing us to go on the offensive” (The Mechanic and the Luddite, 196, 207).  When Axios adds that “this will likely juice historic growth for the winners: the big AI companies, the creators of new businesses feeding or feeding off AI, existing companies running faster and vastly more profitably, and the wealthy investors betting on this outcome,” one can thus hardly be too surprised.

Here, I want to take a slightly different angle however, and think a little bit about the kinds of jobs that are supposed to go away.  It’s hard not to notice the parallels between the Axios list and this one:

Phenomenological Seams (part 3): Governance and the Home

By Gordon Hull

Over a couple of posts (first, second), I’ve used a recent paper by Brett Frischmann and Paul Ohm on “governance seams” – basically, inefficiencies (sometimes deliberate) in sociotechnical systems that are moments for governance – to think about what I called “phenomenological seams,” which are corresponding disruptions in our experience of the world.  I suggested that the combination of the two ideas could be usefully explored by way of Albert Borgmann’s criticism of the stereo as a way to listen to music, rather than direct instrumentation, against the background of Heidegger’s account of breakdowns in our phenomenological experience that occur when tools aren’t as expected.

Borgmann’s objections are notable because of how the stereo, as opposed to the instrument, recalibrates the seams that structure the boundaries of the home.  This is obvious enough in retrospect, in a world with Spotify and wireless earbuds, but the connection shows a couple of things.

First, as Heidegger’s examples also indicate, there is a strong connection between technological governance and phenomenology.   Frischmann and Ohm emphasize that governance seams can be there both to announce their own presence and to achieve transparency. This interruption makes transparent the phenomenological relations that govern a particular experience in the same way that a broken hammer does.  For all that’s wrong with them, the GDPR-mandated cookie notices on websites try to change the experience of websites and nudge users to think about the amount of data they are surrendering.  This example also shows the ways that phenomenological experience can limit the regulatory effect of seams: privacy is way too much work, and users are (as a result) cynical, confused, and disillusioned.  But the cookie requirement establishes a new relation to the websites, precisely because the regulatory seam has phenomenological import.

GOP wants to Deregulate AI completely

The House budget bill is deeply stupid.  No, I don’t mean the massive tax cut extensions for people who don’t need them, done on the backs of food and medical care for the poor, although it also does that and it’s stupid.  I mean the provision that bans states from regulating AI.  Tucked inside is a provision that says “no state or political subdivision may enforce any law or regulation regulating artificial intelligence models, artificial intelligence systems, or automated decision systems during the 10-year period beginning on the date of the enactment of this Act.”  This is making the news if you’re in the right circles, but like so much of the deluge coming out of Washington these days, it’s not getting the national attention it deserves.  Over on Lawfare, Katie Fry Hester and Gary Marcus have the details.  The key point is that the law attempts to pre-empt all state AI regulations.  It would probably also take out big chunks of state privacy laws, just as we’re starting to see some of them.

Hester and Marcus emphasize some of the problems: the bill likely violates the 10^th Amendment, it is absolutely a policy change of the sort that the Senate Parliamentarian should rule out-of-bounds for a reconciliation bill, and it’s deeply unpopular: the public is worried about AI and wants it regulated.  A standard debate about state vs. federal regulation cites the Brandeis “laboratories of democracy” vs. the need for uniform federal rules.  This is a reasonable debate, and it probably depends on the topic which you’d favor.  They are not necessarily exclusive choices either, as federal regulation can set floors and ceilings that states may go above/below, and sometimes federal regulations develop out of state rules.  Federal copyright law preempts state, and that system makes obvious sense.  State laws around gambling or alcohol make sense given the diversity of local cultures.

That debate from your civics class is however not what this is about.  The problem of course is not that we’d have federal policy instead of state.  The problem is that there is absolutely no chance that this Congress will pass meaningful AI regulation, so the choice is between a patchwork of state rules and nothing.  Congress hasn’t even passed meaningful privacy regulation yet, and right now they’re in thrall to an autocrat who issued an Executive Order in his first week in office directing agencies to “suspend, revise, or rescind such actions, or propose suspending, revising, or rescinding such actions” taken in compliance with the Biden Administration’s (bare minimum) efforts at AI regulation, in the name of “revok[ing] certain existing AI policies and directives that act as barriers to American AI innovation, clearing a path for the United States to act decisively to retain global leadership in artificial intelligence.”

Phenomenological seams (part 2): Borgmann, Heidegger and Derrida

Last time, I set up a topic by reading Brett Frischmann’s and Paul Ohm’s “Governance Seams.”  Governance seams are frictions and inefficiencies that can be designed into technological systems for policy ends.  In this regard, “Governance seams maintain separation and mediate interactions among components of sociotechnical systems and between different parties and contexts” (1117).  Here I want to suggest that governance seams have a very close relation to phenomenological ones.  To get there, let me take a detour into an older philosophy of technology paper, Albert Borgmann’s “Moral Significance of the Material Culture.”  Borgmann is concerned with what he takes to be the way that moral and ethical theory ignore material culture, whether they emphasize theory or practice.   Via a paper by Csikszentmihalyi and Rochberg-Halton, he arrives at a distinction between things that he calls “commanding” and “disposable.”  The moral complaint is about the “decline of commanding and the prominence of disposable reality” (294).  Following them, Borgmann distinguishes between a musical instrument and a stereo.

“A traditional musical instrument is surely a commanding thing,” he writes:

“It is such simply as a physical entity, finely crafted of wood or metal, embodying centuries of development and refinement, sometime showing the very traces of its service to many generations. An instrument particularly commands the attention of the student who, unless she is a prodigy, must through endless and painstaking practice adjust her body to the exacting requirements of this eminently sensitive thing.” (294)

After some more similar description, emphasizing the multisensory experience of witnessing someone play an instrument, he turns to the stereo.  Certainly a “stereo produces music as well or, in fact, much better” and some stereos are big.  Nonetheless, “as a thing to be operated, a stereo is certainly not demanding. Nor do we feel indebted to its presence the way we do when we listen to a musician.  We respect a musician, we own a stereo” (295).  The stereo is on the rise, perhaps because “the history of the technology of recorded music is the history of obliging ever more fully the complaint about the burden and confinement of live music,” or “more positively” it is a “promise to provide music freely and abundantly” which is tied to “the promise of general liberty and prosperity – the promise that inaugurated the modern era” (295).

Phenomenological Seams (part 1)

In a recent paper, Brett Frischmann and Paul Ohm introduce the idea of “governance seams,” which are frictions and inefficiencies that can be designed into technological systems for policy ends.  In this regard, “Governance seams maintain separation and mediate interactions among components of sociotechnical systems and between different parties and contexts” (1117).  Their first example is a university’s procedure for anonymous exams.  There, a number of different friction points are added to make sure that professors do not know whose exams they are grading: students receive unique identifiers from the registrar and use only these on the exams; they type but do not write answers; once the exams are scored, the registrar matches the numbers back with individual student names; and so forth.  The professor will likely not be in the room during the exam, so the university will have to provide a neutral proctor.  The exam might also take place in a specified location.  There will also be rules and penalties designed to ensure that none of the seams are crossed without permission.  Together, these governance seams design the system for fairness, or at least to eliminate one potential source of bias in grading.  They’re also pretty inefficient in that they require a bunch of resources be allocated to them, but places with that sort of anonymous grading figure it’s worth it for the fairness bump.

As the paper goes on to argue, such governance seams are ubiquitous and important, because they enable us to design sociotechnical systems to facilitate certain outcomes that might otherwise not happen.  That is, seams open a space for governance:

Privacy and “Injury in Fact,” Part 6: Defamation

As a final installment of reviewing some older “injury in fact” cases, I’d like to look at a few older state libel cases, because the distinction emerges especially clearly in them.  A North Carolina case, for example, noted that “he who publishes slanderous words even as those of a third person with the intent, (to be collected from the mode, extent and circumstances of the publication,) that the charges should be believed, does an injury in fact to the person slandered and ought to answer for it” (Hampton v. Wilson, 15 N.C. 468, 470 (1834).  Here’s a few cases in more detail.  The 19c gender politics is really helpful in seeing how their minds worked on defamation per se.

 

(a) Chastity in Iowa

A pair of Iowa cases are particularly clear.  In Abrams v. Foshee, the Court was asked to rule whether accusing a woman of having an abortion was actionable as slander.  The Court lays out its reasoning particularly clearly:

“To maintain an action of slander, the consequence of the words spoken, must be to occasion some injury or loss to the plaintiff, either in law or fact. As the declaration in this case, claims no special damages, or a loss or injury, in fact, we are left to inquire whether the charges referred to in the instructions refused, was of such a character as to amount to an injury in law. To determine this, it becomes material to ascertain in what cases this action may be maintained, without proof of special damages. Starkie, in his work on Slander, page 9, lays down the rule, that such action may be maintained "when a person is charged with the commission of a crime; when an infectious disorder is imputed; and when the imputation affects the plaintiff in his office, profession, or business." In this case, we only need examine the rule so far as it relates to the charge of a crime. And what is that rule? In Cox and wife v. Bunker and wife, Morris, 269, the Supreme Court of this territory, recognized the rule laid down in Miller v. Parish, 25 Mass. 384, 8 Pick. 384, as the proper one. And in that case it is said, that " whenever an offense is charged, which if proved, may subject the party to a punishment, though not ignominious, but which brings disgrace upon the party falsely accused, such an accusation is actionable. And this is, perhaps, as correct, and at the same time as brief a statement of the general rule, as has been given. For while the rule is variously stated, by different authors and judges, yet in all of them, it is laid down as necessary that the charge shall impute a punishable offense.” (Abrams v. Foshee, 3 Iowa 274, 277-8 (1856)).

That is, if the false statement would have subjected the victim to legal punishment if true, it was considered libel per se – actionable as an act, independent of any damages sustained.  In 1843, “willful killing of an unborn quick child, by an injury, etc., was made manslaughter” (278).  This statute was repealed in 1851. So abortion was not a crime. Plaintiffs urged that the fetus was a “human being” and thus subject to murder.  The Court, at length, disagreed, citing both statute and common law precedents (including Coke and Blackstone) to the effect that abortion was not “murder” even if it were a misdemeanor or otherwise bad.

Tariff policy by chatbot?

I desperately and truly wish that I'd made this up.  Alas, the Verge reports:

"Economist James Surowiecki quickly reverse-engineered a possible explanation for the tariff pricing. He found you could recreate each of the White House’s numbers by simply taking a given country’s trade deficit with the US and dividing it by their total exports to the US. Halve that number, and you get a ready-to-use “discounted reciprocal tariff.” The White House objected to this claim and published the formula it says that it used, but as Politico points out, the formula looks like a dressed-up version of Surowiecki’s method. In case you weren’t sure, Surowiecki calls this approach “extraordinary nonsense.” So why did Trump’s team use it? Well, like plenty of people who’ve realized their homework is due in three hours’ time, it seems like they may have been tempted by AI."

Wait, what?

"A number of X users have realized that if you ask ChatGPT, Gemini, Claude, or Grok for an “easy” way to solve trade deficits and put the US on “an even playing field”, they’ll give you a version of this “deficit divided by exports” formula with remarkable consistency. The Verge tested this with the phrasing used in those posts, as well as a question based more closely on the government’s language, asking chatbots for “an easy way for the US to calculate tariffs that should be imposed on other countries to balance bilateral trade deficits between the US and each of its trading partners, with the goal of driving bilateral trade deficits to zero.” All four platforms gave us the same fundamental suggestion.

There is some variation. Grok and Claude specifically suggested halving the tariff figure to generate what Grok calls a “reasonable” result, much like Trump’s “discount” idea. Ask for a 10 percent baseline tariff and the systems also disagree on whether that should be added to the total tariff rate or not. But answers from across the four chatbots have more similarities than differences.

As I write this, the Dow Jones is down 3.98%. 

Privacy and “Injury in Fact,” Part 5: Early State cases

I’ve been indirectly pursuing the question of the problems faced by privacy plaintiffs in data cases by looking at the origins of the Supreme Court’s standing doctrine.  Basically, plaintiffs have to show an “injury in fact,” and courts often find privacy harms not to meet this standard.  Although presented as dating from time immemorial, the injury in fact requirement was actually announced rather abruptly in 1970 (all of this is part 1).  I’ve been exploring the historical antecedents that will help understand what that language implies – in a very early Supreme Court case (part 2), in other federal case law (part 3), and in federal cases about the Administrative Procedure Act (part 4).  Here I want to extend the genealogy into some early state cases; I’ll draw a somewhat arbitrary cutoff at 1930.  This time I’ll look at a general potpourri of cases. Next time I want to specifically look at a few libel cases because the language is especially clear in them.  I don’t claim this to be exhaustive (and I’m ignoring some of the cases around trusts and deeds because the facts in them are often very confusing, but I think it collectively paints a pretty good picture of what “injury in fact” connoted in Data Processing.

On the whole, the cases point to the legal vs non-legal harm distinction I’ve been developing, As the New Jersey Supreme Court used the concept in an estate case, “there was no injury, in fact or in contemplation of law, to prevent in this case the merger” of the estates (Den ex dem. Wills v. Cooper, 25 N.J.L. 137, 159 (1855).

AI is (still) not a (copyright) Author: The Thaler appellate decision

The Federal Circuit has affirmed the denial of copyright protection to an AI-generated image on the grounds that copyright requires a human author.  As far as I know this was the expected outcome; I certainly think it’s correct.  I talked about the case a bit and made a couple of policy arguments against AI copyright here, when the lower-court ruling came out.

The appellate decision lists several reasons AI cannot be an author: (1) copyright authorship is premised on the capacity to hold property, which AI cannot; (2) copyright duration is tied to the author’s lifespan; (3) copyright includes inheritance conditions, and machines don’t have heirs; (4) Copyright transfer requires a signature, but “machines lack signatures, as well as the legal capacity to provide an authenticating signature;” (5) authors are protected regardless of their “nationality or domicile,” but machines have neither; (6) authors have intentions whereas “Machines lack minds and do not intend anything;” (7) when the copyright act does talk about machines, it always talks about them as tools.

As the court summarizes:

“All of these statutory provisions collectively identify an “author” as a human being. Machines do not have property, traditional human lifespans, family members, domiciles, nationalities, mentes reae, or signatures. By contrast, reading the Copyright Act to require human authorship comports with the statute’s text, structure, and design because humans have all the attributes the Copyright Act treats authors as possessing. The human-authorship requirement, in short, eliminates the need to pound a square peg into a textual round hole by attributing unprecedented and mismatched meanings to common words in the Copyright Act.” (12)

The Prisoner’s Dilemma of AI Cooperation

In a recent piece on Lawfare, Simon Goldstein and Peter N. Salib make the case that AI with cooperation is better than attempting some sort of AI race, unlike what virtually all of the relevant policymakers in the US advocate.  Thus, in response to the Chinese DeepSeek model, US policymakers are doubling down on the idea that the US must “dominate” AI and win against its geopolitical rival.  Goldstein and Salib write:

“In any high-stakes competition to obtain powerful military technology, the closer the game, the more sense it makes to declare a truce and cooperate. Cooperation can help to ensure that both superpowers obtain transformative AI around the same time. This preserves the current balance of power, rather than unsettling it and inviting extreme downside risks for both nations.”

Privacy and “Injury in Fact,” Part 4: Administrative Procedure Act Cases

Here I want to complete my review of federal legal precedents for the Supreme Court’s sudden invocation of “injury in fact” language to understand judicial standing in its 1970 Data Processing decision (recall the earlier installments: first, second, third. The first one explains the issue; if you want to escape my rummaging through the archive, you can skip to this one).  Congress passed the Administrative Procedure Act in 1946 to, well, regulate administrative procedures and provide checks against their being arbitrary (this is one of the Acts that virtually of Trumps recent executive orders violates).  Litigation about agency actions after the APA thus had to route through the APA, which imposed its own standards for judicial review of agency actions.

Here, the language of the lower courts gets very close to the issues in Data Processing.  Consider first Curran v. Laird, in which a maritime union sought enforcement of the Cargo Preference Act, which required that American ships be used for military cargo.  After awarding standing, the DC Cicruit concluded that the decision was a matter for agency discretion under the APA, ruling in favor of the government on the merits.  The Court opens its standing discussion by noting that “plainly [plaintiffs are] aggrieved in fact by the allegedly unlawful action of the Secretary of Defense.” The Court then writes that:

Injury in fact Part 3: Caselaw after Auld

By Gordon Hull

I’ve been pursuing (first, second) what it means for standing law – basically, the determination that someone has a case that can be addressed by an Article III court – to require that plaintiffs show an “injury in fact,” a requirement that emerged suddenly in the Supreme Court’s Data Processing decision in 1970.  The requirement is at considerable odds with the caselaw before it, and it has puzzled commentators.  Last time, I looked at the Supreme Court’s first use of the term, an early 19^th Century case called Hepburn and Dundas v. Auld.  There I suggested that the court’s application of the term to a contract – looking for whether someone suffered harm, outside of the bare violation of a contract terms – wasn’t unlike what the Data Processing court seemed to be doing, in arguing that plaintiffs could demonstrate either legal injury (because of statutory violation) or harm of some other sort (injury in fact).  Here I want to dig into some of the earlier caselaw; what I think that caselaw establishes is that the concept of “injury in fact” generally works to emphasize material harm, as opposed to some sort of statutory or “merely” legal injury.  That sort of contrastive usage isn’t enormously common, but I do think it’s pretty consistent.

There’s three areas where you could talk about this – older cases, regulatory cases, and those relating to the Administrative Procedure Act (APA).  I’ll look at the first two this time, and divide them into Supreme Court and lower court decisions.

Explainable AI as Disciplinary Power

I want to take a break from judicial standing doctrine to note a recent and helpful paper by Emily Sullivan and Atoosa Kasirzadeh about explainable AI.  Explainable AI is a research agenda – there’s a lot of papers and techniques (for a current lit review, see here) – that is designed to get at a central problem in using AI: we often have no idea why machine learning systems produce the outputs they do.  In a variety of contexts, ranging from safety critical systems to democratic governance, being able to understand why the algorithm made the prediction is did is important. Hence the research agenda. 

First, a little detour. Algorithmic governance can be disciplinary in that it can nudge people inexorably toward conforming with norms, whether social or statistical.  Insurance has been well-studied for its normalizing techniques.  In an early paper on privacy unraveling, Scott Peppett showed how the addition of smart-driving surveillance (where insurers give a discount to people who install these devices that record their speed, when they drive, etc.) generate a downward ratcheting on privacy: users who are good drivers have the incentive to adopt the devices, since they get lower insurance rates.  Those who are in the next tier down (above-average drivers) have an incentive to get the devices because that associates them with the good drivers.  And so it goes, until only the worst drivers are declining the surveillance.  And at some point, not having the surveillance device becomes a stigma that raises your rates.  So pretty soon, surveillance devices can become normal.  In the meantime, once drivers have the surveillance installed, surveillance-enabled insurance can nudge them to drive less at night and to otherwise comply with whatever the insurance company says makes you a good risk.  All of that can be automated - the insurance app can tell you, real time, how your driving is impacting your premium.