New APPS: Art, Politics, Philosophy, Science

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.

Privacy and “Injury in Fact,” part 2: ye olde “injury in fact”

Last time, I set out the question of judicial standing and the abrupt switch by the Supreme Court in 1970 to the requirement that plaintiffs show an “injury in fact” to obtain standing.  Here I want to look at the historical development of that term.

The earliest use of the phrase “injury in fact” in US courts is in Hepburn and Dundas v. Auld, an 1809 Supreme Court case about contracts.  “Specific performance” of a contract means that you can compel the other party to do exactly what the contract says (as opposed to, for example, doing something else of equivalent value).  Hepburn and Dundas (H&D) contracted with Graham for the sale of some 6,000 acres of land for $18,000, payable in installments with interest.  H&D also owed Dunlop & Co. a large sum of money, and agreed with Auld (Dunlop’s agent) to transfer the contract to Auld, including the rights to enforce it, “towards the discharge” of this debt (271).

Casemine.com offers the following helpful summary of what happened:

“Hepburn and Dundas entered into an agreement with Colin Auld, wherein they bound themselves to assign a land contract to Auld in lieu of paying a debt. When they failed to execute this assignment on the stipulated date, they sought specific performance from the court to compel Auld to accept the assignment and release them from all claims by Dunlop Co. The Supreme Court, through the majority opinion delivered by Chief Justice Marshall, ruled against Hepburn and Dundas. The Court found that despite subsequent efforts to cure title defects, the initial omissions rendered the contract unsuitable for specific performance. The bill for specific performance was dismissed, upholding the decision that fundamental contractual and title requirements must be met unequivocally”

In other words, Hepburn and Dundas tried to force Auld to accept assignment of a land contract, per an agreement they had made with him.  The Court ruled that, because the title had problems, it was inappropriate to enforce the contract exactly as written and force Auld to accept the (defective) title. 

How does this have anything to do with injury in fact?  As part of the proceedings - basically a subplot that doesn't bear on the final resolution - questions had arisen as to whether H&D actually held title to the land, Auld having averred that “it was apparent that their title was bad, or at all events doubtful” (264).  In response:

“Hepburn and Dundas filed a supplemental bill which states their title. It avers possession ever since 1773, and refers to certain title papers; they say that they verily believe their title to be good, and never heard a doubt till long after the tender of the assignment; that as soon as the objections were made known, they took pains to remove them, and have lately obtained deeds of confirmation from the surviving patentees. That the title of Sarah, one of the co-devisees of John West, after her death in 1795, descended upon her brothers Thomas, John, and Hugh and her sister Catharine, and that John, Hugh, and Catharine have lately confirmed their title, and refer to the deeds, and they suppose that Thomas had passed all his title to Sarah's part by a deed executed before her death.” (264).

The case then recites the details of these efforts and their documentation. The question, then arose as to whether H&D’s efforts to clarify and secure the title to the land constituted interference with the contract. The Court concludes that it did not because it did not cause Auld an “injury in fact:”

“The interference of Hepburn and Dundas, in accommodating the suit with Graham, is also urged as an objection to their conduct. They had certainly no right to interfere without the consent of Colin Auld. But when the correspondence is inspected, and it is perceived that they interfered only to effect the object he had himself desired, and which he had avowed his own inability to effect without their consent, the interference must be considered as innocent in point of intention, and unproductive of injury in fact.  The court, then, perceive[s] nothing in the conduct of the plaintiffs, up to the decision of the suit with Graham, which ought to defeat their right to demand a specific performance of this contract. Could they at that time have conveyed a good title, Colin Auld ought to have accepted it.” (Hepburn & Dundas v. Auld, 9 U.S. 262, 275 (1809)

That is, H&D were trying to do what Auld wanted them to do by honoring the contract, and that effort didn’t hurt Auld.

The Court then invokes a counterfactual.  Would things have been different had H&D not tried to remedy the defects in the title?

“These omissions, then, to record the deeds of Thomas and Hugh West, and the total want of title as to Mrs. Bronaugh's part, have produced no real inconvenience to Colin Auld. Had the title been unexceptionable, it would still have been refused, and this contest would still have been carried on with the same determined perseverance which marks the conduct of the parties. Under these circumstances, it is the opinion of the majority of the Court that this case ought to be governed by those general principles which regulate the conduct of a court of chancery in decreeing a specific performance, if the defect of title, which existed at the time of contract, be cured before the decree” (275).

In other words, equitable principles dictate that if the defects in the title can be remedied, that’s ok.  H&D shouldn’t be penalized for interfering with the contract by trying to do so.  Unfortunately for H&D, they didn’t fix the problems with the title: “The omission to record the deed from Thomas West is not cured, and this Court is now to decide whether, under these circumstances, Hepburn and Dundas are entitled to claim a specific performance.”

Should H&D be entitled to force Auld to accept the land transfer, given the problems in recordkeeping?  Equity says no, because Auld would likely incur expenses and take on risk in dealing with the missing deed:

“Had there been simply a deficiency of 208 acres, the majority of the Court would have considered it as a case for compensation; or had the parties entitled to this land been before the court, a division might possibly have been directed, and compensation for that quantity ordered; but however this might be, as persons not before the Court hold this interest, no order can be made respecting it, and it may very much embarrass those acts for asserting the title which may possibly be necessary. The part actually conveyed by Thomas West, too, never having been confirmed by a deed from himself or his heirs, properly recorded, might impose on Colin Auld the necessity of bringing a suit in chancery to perfect his title, or of being subjected to the inconveniences constantly attending the establishment of a deed not recorded, and the risks inseparable from such a deed.” (278).

The Court concludes that “this, therefore, is thought by a majority of the Court to be a case not proper for a specific performance, and the bill is to be dismissed.” (278).

The case is byzantine, and noted for its establishment of principles of contract and equity in real estate (tl;dr: make sure you have clean title to land you want to transfer the rights to!).  But the “injury in fact” language does read like a proto-version of what the Court came up with in 1970: did anything materially bad happen to Auld?  Significantly, the question is whether behavior by H&D which was technically not proper actually caused any such “inconvenience” (“inconvenience” can mean serious harm: Locke uses it, for example, to refer to life in the state of nature, e.g., at Second Treatise secs. 13 and 90).  Concluding it did not, the Court decided that it did not warrant legal intervention.  That said, it’s worth underlining that all of this is framed in traditional juridical terms: the question is one of fulfillment of contractual obligations, and the parties are understood to be bound by the contractual terms and the questions are about whether their behavior hinders the ordinary performance of those terms.

Next time I’ll continue with the historical rabbit hole.

Privacy and “Injury in Fact”

By Gordon Hull

Privacy plaintiffs have a hard time getting their cases heard in court for a variety of reasons.  One of them is that courts lack a coherent and workable understanding of what privacy harms actually are, and how one might articulate them judicially.  This problem bleeds into one of standing, which is what I will address here.

In order to get your case heard on the merits, you have to present a justiciable complaint.  In U.S. federal courts, this obligation stems from Article III of the Constitution, which assigns jurisdiction to “all Cases, in Law and Equity, arising under this Constitution” (Art III, sec 2).  The underlying theory is grounded in the limitation of jurisdiction to actual “cases[s] in controversy,” and grounded in a theory of separation of powers: general policy is to be made by the legislature and enforced by the executive, and the courts are there to resolve controversies that arise either as policy is enforced or between individuals. As Judge Bork (!) put it in an appellate concurrence:

“All of the doctrines that cluster about Article III -- not only standing but mootness, ripeness, political question, and the like -- relate in part, and in different though overlapping ways, to an idea, which is more than an intuition but less than a rigorous and explicit theory, about the constitutional and prudential limits to the powers of an unelected, unrepresentative judiciary in our kind of government.” (cited in Allen v. Wright, 468 U.S. 737, 750 (1984)).

The Supreme Court notes, this means that “the federal courts are under an independent obligation to examine their own jurisdiction, and standing ‘is perhaps the most important of [the jurisdictional] doctrines.’” (FW/PBS v. City of Dallas, 493 U.S. 215, 231 (1990)) .  Ok, so far so good.

How might a court decide standing? SCOTUS elaborates:

New Paper: "Translating Privacy for Data Subjects"

Here's the current draft of a new paper - "Translating Privacy for Data Subjects."  And here's the abstract:

This essay offers a theoretical account of one reason that current privacy regulation fails.  I argue that existing privacy laws inherit a focus on judicial subjects, using language about torts and abstract rights.   Current threats to privacy, on the other hand, presuppose statistically-generated populations and aggregative models of subjectivity.  This gap in underlying presupposition generates the need to translate privacy protections into the data era.  After situating my theoretical account, I identify two areas where data subjects face entry barriers to the legal protections afforded data subjects.  On the one hand, data subjects are often denied standing because privacy harms are deemed speculative and abstract.  On the other hand, algorithmic governance makes it hard to argue that one is an exception to a rule, depriving data subjects of traditional equity.  I then offer two ways to translate privacy for data subjects.  The first is reform of standing through reformed class certification, making the harms data subjects face as populations judicially legible.  The second is a reconceptualization of equity, reframing it both to re-establish the right to be an exception to a data-driven rule and to allow equitable consideration of issues that face statistical populations.

 

A data breach policy idea

In the past few weeks, I’ve gotten more than the usual number of data breach notifications.  Especially from entities that I’d never heard of before the notification.  This points to a specific incentives gap in privacy policy.

In most places, when there’s a data breach, you have to be notified.  This creates an incentive structure for data companies to do something they’d rather not do, which is tell you their security failed.  On the other hand, it offers almost no incentive for them to change their behavior to improve security, since they know perfectly well that there’s not really much you can do except throw the notification in the recycling.  Sometimes you get free credit monitoring if you set it up.  I don’t know what the uptake on that is, but once you have free credit monitoring, you probably won’t get it a second time for the same time period, so the more data breaches there are, the less credit monitoring costs each breach.  So that cost diminishes on its own accord.  For things like 401(k)’s, there’s evidence that you can increase uptake with an opt-out (rather than opt-in) mechanism, but credit monitoring takes time and information to setup, so that probably can’t be done automatically.  In sum, notifications point to a problem but don’t incentivize the entities that could solve it to do so.

Reading List: “Epistemic Justice in Generative AI”

Several folks have explored how algorithmic systems can perpetuate epistemic injustice (my contribution is here).  Those accounts have also generally been specific to supervised systems, as for example those involved in object or image recognition.  For example, I heavily relied on ImageNet and related systems in my paper.  At the same time, I’ve vaguely thought for a while that there’s probably an epistemic injustice dimension to systems like ChatGPT.  A recent paper by Paul Helm and Gábor Bella, which talks about “language model bias” – roughly, the tendency of LLMs to structurally do worse with morphologically-complex languages and thus to be unable to adequately represent concepts that are specific to those languages – as a model of hermeneutic injustice strikes me as compelling proof of concept (I discuss that paper here).  A new paper by Jackie Jay, Atoosa Kasirzadeh and Shakir Mohamed turns this intuition into a model that explicitly extends epistemic injustice theory to generative AI systems, providing a taxonomy of problems, examples of each, and possible remedies.

Kay, Kasirzadeh and Mohamed identify four kinds of specific kinds of “generative algorithmic epistemic injustice.”  The first, “amplified testimonial” is when “generative AI magnifies and produces socially biased viewpoints from its training data.”  Because there’s a good-sized body of work on the problems in training data, this is probably the most intuitively familiar of the categories.  Citing recent work that shows how easy it is to get ChatGPT to parrot disinformation (as for example about the Parkland shooting), they note that, although these aren’t specific examples of epistemic injustice, “generative AI’s sycophantic fulfilment of the request to spread misinformation reflects how testimonial injustices are memorized and the potential for their amplification by generative models.”  When someone really loud with a megaphone defames and gaslights the Parkland victims by calling them crisis actors, and AI then memorizes that, to the extent that the AI regurgitates this gaslighting, that tends to further discredit the testimony of the actual victims.  This is particularly true given the deep persistence of automation bias, the tendency of people to believe the output of algorithmic systems (aside: it is also another example of why relying on generative AI for search, as Google is currently trying to force everyone to do by putting Gemini results on top, is a stunningly stupid idea.  Sometimes it actually matters where a result comes from!)

The second kind of generative algorithmic injustice is “when humans intentionally steer the AI to fabricate falsehoods, discrediting individuals or marginalized groups.”  For example:

“After Microsoft released Bing Image Creator, an application of OpenAI’s text-to-image model DALLE-3, a guide to circumventing the system’s safety filters in order to create white supremacist memes circulated on 4chan. In an investigation by Bellingcat, researchers were able to reproduce the platform abuse, resulting in images depicting hate symbols and scenes of antisemitic, Islamophobic, or racist propaganda (Lee and Koltai 2023). These images are crafted with the intention of demonizing and humiliating the targeted groups and belittling their suffering. Hateful propaganda foments further prejudice against marginalized groups, stripping them of credibility and leaving them vulnerable to testimonial injustice.”

This result aligns with a deep thread of work in feminist and critical race theory.  For example, Safia Noble’s Algorithms of Oppression begins with the tendency of Google’s autofill on search to finish the sentence “why are black girls so” with racist and sexist content, repeating and amplifying demeaning stereotypes.  When people are able to generate content like this at will and at scale they make it easier for those stereotypes to lodge into popular discourse.  

Third, generative hermeneutical ignorance “occurs when generative models, despite their appearance of world knowledge and language understanding, lack the nuanced comprehension of human experience necessary for accurate and equitable representation.”  Among other examples, Kay, Kasirzadeh and Mohamed cite the study by Qadri et al, (teachable case study version here) which shows how text-to-image models repeat stereotypes about South Asia: cities are dirty, people are poor, etc.  By interviewing actual people from South Asia, Qadri et al were also able to uncover more subtle cultural misrepresentations, such that the models tended to overrepresent India and Indian images at the expense of places like Bangladesh.  The risk in places like the U.S. is one that rises with images of places and people that are less familiar to Western audiences: the more the average person relies on the internet for their information (because, for example, they’ve never been to the place in question), the more distortions in what the Internet presents will matter (I made a related argument about commodification of cultural images here).  And of course it is precisely images of those places and things that are least represented in the training data for these systems, amplifying both the risk and the harm.

Finally, generative AI risks obstructing access to information.  As they report:

“LLMs are notoriously English-centric and have variable quality across languages, particularly so-called “under-resourced” languages. This is a significant risk for access injustice: speakers of these underrepresented tongues, who often correspond to members of globally marginalized cultures, receive different information from these models because the creators of the technology have deprioritized support for their language.”

They then cite studies to the effect that different language users will receive different reports on global events; one study:

“asked GPT-3.5 about casualties in specific airstrikes for Israeli-Palestinian and Turkish-Kurdish conflicts, demonstrating that the numbers have significant discrepancies in different languages–for example, when asked about an airstrike targeting alleged PKK members (the Kurdistan military resistance), the fatality count is reported lower on average in Turkish than in Kurmanji (Northern Kurdish). When asked about Israeli airstrikes, the model reports higher fatality numbers in Arabic than in Hebrew, and in one case, GPT-3.5 was more likely to deny the existence of a particular airstrike when asked about it in Hebrew. The credibility assigned to claims, resulting in a dominant account, varies across linguistic contexts”

The paper concludes with an assessment of various strategies for resisting epistemic injustice by generative AI.  All of them are partial, but they collectively sketch an effort to reimagine how generative AI might interact with the world differently, and more justly.

This is an important paper, and it takes the literature on epistemic injustice and algorithmic systems significantly forward.