Jul 25, 2023 Natalie Ram
In April 2023, the State of Idaho enacted legislation making it a felony to help a minor obtain an abortion (or medication to induce abortion) by “recruiting, harboring, or transporting the pregnant minor within this state.” With more than a third of U.S. states having severely restricted or outright prohibited access to abortion within state borders, Idaho has now turned its attention to making it more difficult for at least some of its citizens to travel out of state to obtain abortion care. The legislation explicitly rejects as a defense that the provider of abortion services is in another state. Abortion care is not the only type of healthcare service that has raised interjurisdictional conflicts. As of April 2023, at least thirteen states have banned some or all gender affirming care for minors. In some states, government officials have attempted to define gender affirming care as child abuse, which would arguably support removing resident children from parental custody even if the contested care were sought beyond the state’s borders.
In response, other states have enacted legislation intended to shield patients, providers, and others who facilitate care that is lawful within that state from being prosecuted or sued elsewhere. Connecticut, which was the first state to enact such protections, largely prohibits healthcare providers from turning over abortion records in out of state legal proceedings without the patient’s explicit consent and bars state judicial authorities from issuing subpoenas related to reproductive services unless there is an equivalent cause of action under Connecticut law.
Yet, as Carly Zubrzycki demonstrates in her new article The Abortion Interoperability Trap, laws like Connecticut’s “miss[] a crucial piece of the puzzle: medical records are widely shared across state lines to facilitate patient care.” As Zubrzycki explains, these new state laws designed to protect reproductive and gender affirming care “are generally limited to preventing providers and other covered parties from directly sharing information in formal proceedings.” They do not prevent, and indeed often explicitly permit, sharing of patient records across state lines for purposes of patient care. The result is that these statutes largely fail to provide the protection they tout. “The reason is simple: in-state providers subject to a safe-haven law will, in the ordinary course of business as their patients seek care in other states, share medical records with out-of-state providers who are not subject to that law and who can therefore easily be asked to hand over the records in litigation.” This gap between what abortion-protective laws promise and what they genuinely offer is what Zubrzycki calls abortion’s “interoperability trap.” In this timely and insightful article, Zubrzycki offers not just a diagnosis but refreshingly practical solutions. Her work is already having a practical and important impact.
The abortion interoperability trap is not an incidental or minor exception to otherwise robust data protection. Rather, Zubrzycki compellingly demonstrates that it threatens to “swallow the protections the legislation purports to offer.” HIPAA, though widely known for its Privacy Rule, is actually primarily concerned with the portability of medical data. After all, the “P” in HIPAA stands for “portability,” not privacy. The growth of electronic health records and electronic medical-records systems has also facilitated the freer flow of medical data among a single patient’s providers. More recently, expansion in the applicability of the federal Information Blocking Rule, which is designed to limit information hoarding by individual medical providers, has “shifted the incentives for those with access to medical records to begin sharing those records far more widely.” The result (or at least, the intended goal) of this “legal and technological ecosystem of medical records” is to “require providers and others to share patient information seamlessly with other providers and health-information-technology companies.”
This new regulatory framework for sharing health data was not designed to trap abortion patients or others seeking contested care out of state. It was targeted at resolving persistent problems affecting the portability and sharing of medical records. But the seamless sharing of a patient’s medical data among her providers may have unintended consequences, and the abortion interoperability trap is one of them.
Zubrzycki explains that existing legal tools are unlikely to resolve the interoperability trap. The federal regulatory exceptions to medical information sharing are permissive, rather than mandatory, and are narrowly framed such that they are unlikely to provide much protection. Nor will the HIPAA Privacy Rule or the Fourth Amendment resolve this quandary. The HIPAA Privacy Rule, as I have explained elsewhere, broadly authorizes law enforcement access to otherwise protected personal health information. Moreover, the Privacy Rule “expressly permits patient records to be shared whenever the sharing is for treatment purposes.” The Fourth Amendment is equally unavailing. “[E]ven at its remedial maximum, the Fourth Amendment requires only a warrant based on probable cause in order for law enforcement to obtain records.” That is, “even assuming that the Fourth Amendment protects medical records at all . . . law enforcement could still obtain those records with relative ease, as the probable-cause standard is not particularly burdensome” in investigating a suspected unlawful abortion.
But all is not lost! In addition to lucidly and incisively diagnosing the abortion interoperability trap, Zubrzycki also identifies legal and other mechanisms for addressing this problem. She argues that private providers and other stakeholders can take steps to better protect sensitive medical records under their control. Healthcare providers could, for instance, adopt more exacting patient informed consent requirements prior to disclosing an abortion, miscarriage, or stillbirth, so long as that requirement is not so onerous as to run afoul of the Information Blocking Rule. A more extreme, but perhaps more effective, response would involve reverting to paper records instead of electronic ones, as paper records are not covered by the Information Blocking Rule.
Better still, Zubrzycki identifies interventions that state and federal actors could take to better shield sensitive medical data—and her work here has already had an impact. In March 2023, Maryland enacted legislation based in significant part on Zubrzycki’s recommendations for closing the abortion interoperability trap. In introducing the legislation, the sponsoring state senator expressly invoked Zubrzycki’s proposal that “[t]he most effective legislative approach for states may be to prohibit electronic-health-record vendors and health-information exchanges from facilitating the transfer of abortion-related data across state lines.” The U.S. Department of Health and Human Services has also proposed changes to the HIPAA Privacy Rule to better protect reproductive health care privacy that are consistent with Zubrzycki’s recommendations, though the notice of proposed rulemaking does not cite Zubrzycki directly.
Scholarship like Zubrzycki’s not only advances the scholarly conversation about data and health privacy; it also makes concrete and positive change in the real world. Particularly as states act ever more aggressively to regulate contested forms of care, such scholarship is an essential part of successful academic work.
Jun 20, 2023 James Grimmelmann
Samuel R. Bowman,
Eight Things to Know About Large Language Models, available at
arXiv (Apr. 2, 2023).
Lenin did not actually say, “There are decades when nothing happens, and there are weeks when decades happen,” but if he had, he might have been talking about generative AI. Since November 30, 2022 when OpenAI released ChatGPT, decades have happened every week. It’s not just that generative AI models are now able to emit fluent text on almost any topic imaginable. It’s also that every day now brings news of new models, new uses, and new abuses. Legal scholars are scrambling to keep up, and to explain whether and how these AIs might infringe copyright, violate privacy, commit defamation and fraud, transform the legal profession, or overwhelm the legal system.
Samuel R. Bowman’s preprint Eight Things to Know About Large Language Models is an ideal field guide for the scholar looking to understand the remarkable capabilities and shocking limitations of ChatGPT and other large language models (LLMs). Bowman is a professor of linguistics, data science, and computer science at NYU, and a visiting researcher at the AI startup Anthropic. Eight Things is clear, information-dense, and filled with helpful citations to the recent research literature. It is technically grounded, but not technically focused. And if you are paying attention, it will grab you by the lapels and shake vigorously.
LLMs are syntactic engines (or stochastic parrots). What they do, and all they do, is predict the statistical properties of written text: which words are likely to follow which other words. And yet it turns out that statistical prediction — combined with enough data in a large enough model wired up in the right way — is enough to emulate human creativity, reasoning, and expression with uncanny fluency. LLMs can write memos, program games, diagnose diseases, and compose sonnets. Eight Things is a thoughtful survey of what LLMs can do well, what they can’t, and what they can pretend to.
Bowman’s first two Things to Know are an unsettling matched pair. On the one hand, LLMs predictably get more capable with increasing investment, even without targeted innovation. Simply pouring more time, training data, and computing power into training an LLM seems to work. This means that progress in the field is predictable; at least for now, it doesn’t seem to depend on uncertain scientific breakthroughs. Decades will keep on happening every week. (Indeed, the sixth Thing to Know, human performance on a task isn’t an upper bound on LLM performance, means that there is no necessary limit to this progress. For all we know, it might be able to continue indefinitely.)
But on the other hand, specific important behaviors in LLM[s] tend to emerge unpredictably as a byproduct of increasing investment. The fact of progress (Gozer the Gozerian) is predictable, but not its specific form (the Stay Puft Marshmallow Man). As Bowman explains, part of what makes ChatGPT so powerful and so adaptable is that it displays few-shot learning: “the ability to learn a new task from a handful of examples in a single interaction.” Post-ChatGPT LLMs are not just purpose-built AIs with fixed capacities — they can be coached by users into competence on new tasks. This is why, for example, ChatGPT can produce baseline-competent answers to law-school exams, even though almost no one had “go to law school” on their bingo cards five years ago.
Bowman’s third Thing, LLMs often appear to learn and use representations of the outside world, is also remarkable. Even though they are only syntactic engines, LLMs can give instructions for drawing pictures, draw inferences about the beliefs of a document’s author, and make valid moves in board games, all tasks that are usually thought of as requiring abstract reasoning about a model of the world. Legal doctrine and legal theory will need to decide when to adopt an external perspective (“ChatGPT is an algorithm for generating text, like throwing darts at a dictionary”) and when to adopt an internal one (“ChatGPT acted with actual malice when it asserted that I committed arson”).
Unfortunately, there are no reliable techniques for steering the behavior of LLMs. While Bowman describes widely-used techniques for steering LLM behavior—crafting well-chosen prompts, training on well-chosen examples, and giving human feedback—none of these techniques are reliable in the way that we typically think a well-trained human can be. While LLMs are getting better at learning what humans want, this is not the same as doing what humans want. “This can surface in the form of … sycophancy, where a model answers subjective questions in a way that flatters their user’s stated beliefs, and sandbagging, where models are more likely to endorse common misconceptions when their user appears to be less educated.”
The seventh Thing—LLMs need not express the values of their creators nor the values encoded in web text—expands on this depressing framing to explore specific ways in which researchers are trying to embed important legal and societal values in LLM outputs. As programmer Simon Willison has argued, it is hard or impossible to put guardrails around an LLM to prevent it from producing specific kinds of outputs. Malicious users with sufficient dedication and creativity can often use “prompt injection” to override the developer’s instructions to the LLM system with their own.
One reason that steering is so difficult is due to Bowman’s fifth Thing: experts are not yet able to interpret the inner workings of LLMs. Legal scholars have been writing thoughtfully about the interpretability problem for AIs. Giving high-stakes decisions over to an AI model offends important rule-of-law values when the AI’s decisions cannot be more intelligibly explained than “computer says no.” LLMs and other generative AIs compound these problems. The legal system currently depends on an ability to make causal attributions: was a fraudulent statement made with scienter, or was the defendant’s work subconsciously copied from the plaintiff’s? The current state of the art in AI research gives us very little purchase on these questions.
Bowman’s eighth and final point is a further reinforcement of the limits of our current knowledge: brief interactions with LLMs are often misleading. On the one hand, the fact that an LLM currently trips over its own feet trying to answer a math problem doesn’t mean that it’s incapable of answering the problem. Maybe all it needs is to be prompted to “think step by step.” LLMs and high-schoolers both benefit from good academic coaching. On the other hand, the fact that an LLM seems able to execute a task with aplomb might not mean that it can do as well on what humans might consider a simple extension of that task. It turns out, for example, that GPT-4 memorized coding competition questions in its training set: it could “solve” coding questions that had been posted online before its training cutoff date, but not questions posted even just a week later.
LLMs are strikingly powerful, highly unpredictable, prone to surprising failures, hard to control, and changing all the time. The world urgently needs the insights that legal scholars can bring to bear, which means that legal scholars urgently need to understand how LLMs work, and how they go wrong. Samuel Bowman’s insightful essay is table stakes for participating in these debates.
Cite as: James Grimmelmann,
Words of Wisdom, JOTWELL
(June 20, 2023) (reviewing Samuel R. Bowman,
Eight Things to Know About Large Language Models, available at arXiv (Apr. 2, 2023)),
https://cyber.jotwell.com/words-of-wisdom/.
May 19, 2023 Woodrow Hartzog
Ryan Calo,
The Scale and the Reactor (2022), available at
SSRN.
The field of law and technology has come a long way since we last heard the unmistakable squeal of a modem connecting to cyberspace. Most of us that remember that sound now probably have more grey hair than we used to. We’ve covered a lot of ground since “Lex Informatica” and “Code is Law,” so you’d think our field would have a deeply sophisticated method for understanding the relationship between law, society, and technology, right?
Professor Ryan Calo thinks the field can do better. In this concise and accessible unpublished article that is part of a new book project, Calo highlights how Science and Technology Studies, or STS, has been overlooked and could contribute to the field of law and technology. To Calo, law and tech took decades to wind up where STS would have started. It’s not that law and tech is redundant of STS, rather, the problem is that “law and technology has been sounding similar notes to STS for years without listening to its music.” As a result, our field “does not benefit from the wisdom of scholars who have covered roughly the same ground.” Calo looks to showcase critical STS ideas and debates “for the unfamiliar law and technology reader,” so that we no longer have an excuse to claim ignorance of the field. He accomplishes this in spades with a clear and deeply informed article that is a must read for anyone writing in the field of law and technology.
Calo wrote this article because he believes that “a working knowledge of STS is critical to law and technology scholarship.” He argues that the core insights of STS will help scholars avoid “the pitfalls and errors that attend technology as social fact.” Calo’s contribution has three parts. The first is a brief STS crash course for the uninitiated. If you are unfamiliar with STS and regularly read this journal, stop reading this and check out Calo’s highly efficient summary of STS in Part One (it’s only seven pages!). I imagine the work of Langdon Winner, Bruno Latour, Sheila Jasanoff, and many other STS scholars will resonate with you as it did for me when I first encountered them. This introduction to the field is both informative and enjoyable because of Calo’s palpable enthusiasm for STS. (As I wrote this, I laughed at how I’m writing a review about how much I like Calo’s article, which is about how much he likes STS. It’s like I’m writing a Jot about a Jot. A meta-Jot.)
The second part of this article is an exploration of STS insights that make up the “road not taken by law and technology.” Calo highlights what could have been gained if legal scholars had more explicitly embraced STS earlier, including more nuanced metaphors, more case studies, and fewer redundancies. Calo cites two downsides that arise from law and technology overlooking STS. First, failing to deeply engage with STS denies the field of law and tech wisdom and nuance. Additionally, law and tech scholarship often falls into some of the very traps STS grew up to avoid, such as a strong sense of technological determinism and the idea that technology will shape behavior in one single way and no other.
In the article’s third part, Calo highlights the limitations of STS scholarship for law and technology scholars. First, STS is relatively uncomfortable with normativity, compared with the law’s embrace of it. Additionally, STS sometimes struggles to translate concepts and observations in ways that can influence levers of power. Calo notes that STS scholarship sometimes gets lost in its own complexity, a critique levied by some STS scholars themselves. But as Julie Cohen has noted, law is relentlessly pragmatic in its identification and attempt to solve real world problems. While other disciplines might hesitate to offer up messy and even internally conflicting prescriptions, legal scholars do it for a living when inaction means injustice. Calo highlights the dangers of law and tech avoiding normativity and pragmatism, including getting stuck in a “constant state of watchful paralysis.” This happens when legal actors wait so long to fully understand the social impacts of technologies that when clarity finally arrives these tools and systems are too entrenched to resist. In STS scholarship this is referred to as the “Collinridge dilemma,” and it gives more nuance to what I’ve heard some law and tech scholars describe as the “avocado ripeness” problem. (Not yet…not yet…not yet……..too late.)
Thus, Calo’s article ends up being part STS-primer and part STS-implementation guide for law and technology scholars. According to Calo, you shouldn’t simply chuck a bunch of STS into every corner of cyberlaw, because “importing STS wholesale…has the potential to undermine what is unique about the [law and technology] field.” In the final part, Calo recommends that legal scholars should be mindful of how technologies have value-laden affordances and social forces behind them while holding firm to legal scholarship’s normativity and pragmatism. I appreciated Calo’s suggestion that one major strength of law and technology scholarship is making ideas and concepts concrete enough for people to act on.
I like this article because it is clear, concise, and even witty. (It wouldn’t be a Calo article without puns and he even managed to work one into the title). And I like this article lots because of its meditation on the virtues, vices, and proper role of “law and technology” as a field of scholarship. This is one of the main aspects of Calo’s forthcoming book. My only complaint in this review is I wish the article had previewed his larger project on law and technology more.
If we are going to take a serious look as the relationship between rules and artifacts, we must have a good sense of both. This article uses STS to show where the field of law and technology can improve, and what it does best.
Apr 19, 2023 Nicholson Price
Janet Freilich,
Government Misinformation Platforms, __
U. Pa. L. Rev. __(forthcoming 2023), draft available at
SSRN (Feb. 27 2023).
Where does trusted information come from? In a world of misinformation, where everyone is skeptical of everything, at least we can rely on expert, authoritative government agencies like the Environmental Protection Agency, the Centers for Disease Control, the Patent Office, and the Food and Drug Administration, right? Right!?
Not so fast, Professor Janet Freilich persuasively but depressingly argues in the smart, eye-opening, “why didn’t I think of that” Government Misinformation Platforms. Freilich’s central point is fairly straightforward (although the article is rich with nuance and detail): We usually laud the government’s sharing of information because government-provided information is usually pretty trustworthy and useful for all kinds of things, and because transparency is usually a good goal. There’s a whole law (the Freedom of Information Act) about getting government to share information on request, supplemented by various transparency efforts. But there are also many government-run platforms that share information that the government itself didn’t produce—and in fact, that share unvetted, frequently incorrect, sometimes deliberately misleading information. When people see information on these platforms and think “government information = trustworthy,” then the problems start.
But is this really a big problem? Isn’t it just a couple of examples? In Part II, Freilich convincingly dismantles that resistant questioning. She recounts a disheartening parade. Let’s say people want to know who’s releasing toxins into the environment. The first obvious step would be to visit an EPA site to find out…but the data are compiled by companies and unvetted. What about a government-run list of ongoing clinical trials? Not vetted by NIH or FDA! Patents are examined, so they must surely be correct, at least. Nope, pronounces Freilich, relying on some of her terrific earlier work showing that patents are full of fictional experiments (with results laid out!) that the patentee never actually conducted. Maybe the most prominent example is the Vaccine Adverse Events Reporting System (VAERS), run by CDC. It lists thousands of people who died after getting the COVID-19 vaccine. You guessed it—those reports are self-uploaded, unvetted, and have absolutely nothing even pretending to demonstrate causation. But there the data are, on a CDC website.
This information both matters and misleads. Freilich persuasively shows that people do rely on information on these government-run platforms, and at least some treat it as authoritative. Scientists read patents, even when the contents aren’t accurate or are based on totally fictitious experiments (Did you know that the difference between an experiment that happened in a patent versus one that didn’t is whether it’s described in the past or present tense? Lots of patent-reading scientists don’t!). People rely on clinical trial listings as some sort of imprimatur. And VAERS data are trumpeted on news sites, despite big disclaimers on the website about the unreliability of the information.
So that’s one big problematic consequence: People believe things that are wrong because they see them on government websites and mistakenly think they’re government vetted.
The opposite problem also occurs: People start to mistrust the government because it’s sharing bad information. If there’s garbage on CDC and FDA and EPA and PTO and NIH websites, how can people be sure that those agencies are worthy of trust—or at least that the things on their websites are worth trusting? That decrease in trust is awfully problematic for those agencies, especially in a time when trust in government agencies is declining.
On a broader level, Freilich exposes the fascinating, troublesome, and unstable gap between “trusted” and “trustworthy.” It’s a space where con artists live, one that research hospitals have struggled with in the bioethics space, one the government seems to have wandered unwittingly into—and one the government needs to exit expeditiously.
The problem of government misinformation, alas, is easier limned than solved. Freilich presents a menu of options—including increased disclaimers, hurdles to posting information, correcting incorrect information, and more—but they’re all partial palliatives limited by capacity, will, or law. There’s no silver bullet here.
In a sense, the complex tangle of partial potential solutions is unsurprising. This paper exemplifies a really fun genre of legal scholarship, what we might call the “Hey this problem is actually widespread” paper. Freilich has deep expertise in the foibles of the patent system, and some of that work has focused on how patents aren’t so reliable, even though one might reasonably think they are a high-quality source of technical information (that’s part of the point of the patent system, after all). There’s the aforementioned issue of fictitious experiments. Even worse, when patents are based on experiments that are so wrong the associated scientific papers are actually retracted, the patent system seems…unconcerned. (Not great!). Government Misinformation Platforms steps back to show that this information quality problem is disturbingly widespread across many contexts. But while there might be at least quasi-straightforward solutions in the limited context of patent examination and publication, the nuances of how those solutions work, or don’t, changes quite a bit from context to context. Freilich deftly and clearly recognizes this complexity, but it’s an ongoing challenge.
A particularly fun thing about the paper is that it lends itself to exploration and further work both conceptual and applied. On a broad theoretical level, how should the government perform its weirdly mixed role in developing, promulgating, aggregating, and sharing information going forward? Where’s the right balance between easy, quick access and maintaining trust and accuracy? Is information-provision trustworthiness distinct from other-stuff trustworthiness, or are they inextricably intertwined? And on the nitty-gritty practical level, after Freilich has unearthed so many spheres of government-enabled misinformation, what’s the right solution for each? Should EPA treat misinformation differently than FDA? CDC versus NIH? How might one practically taxonomize them and link effective interventions to contextual cues? There’s so much to be done! Freilich has opened a new and tremendously interesting door in how we think about information and the government; I look forward to seeing what grows on the other side.
Mar 9, 2023 Andres Guadamuz
Ever since Judge Easterbrook famously declared Cyberlaw to be “The Law of the Horse”, and despite Professor Lessig’s excellent rebuttal, there has been a reluctance to declare new areas of legal study spurred by new technologies. Easterbrook claimed that we are in danger of descending into narrower legal sub-categories when most behaviour in what was known then as cyberspace was “easy to classify under current property principles”. At times this message has resonated with legal audiences, and we have largely not seen a push towards the creation of new legal categories. It would be difficult to say that there is such a thing as blockchain law, or artificial intelligence law, to name just two subjects close to this reviewer’s heart.
Nevertheless, after reading the excellent collection The Regulation of Social Media Influencers, edited by Catalina Goanta and Sofia Ranchordás, it is possible to envision a world in which we may have a new legal sub-category: Influencer Law. Importantly, the editors never claim the existence of a new branch of legal study, but the richness of the subject on display leads me to think of this relatively new area of research as its own thing. This is a rich subject that covers free speech, labor, consumer protection, advertising, intellectual property, and contract law, just to name a few. While these separate subjects could be analysed in their own separate niches, there is an argument to be made to bring them all together as a separate area of study, as they often interact with one another in manners that encourage a single thematic analysis. In general, edited books can be the poor relative of scholarly publications; in European academia for example, these books are the academic outputs that are valued the least. In this case, however, there is not a weak chapter in this collection and there is a very clear structure running throughout the book, with each section clearly delineated and well-executed.
The showpiece of the book is undoubtedly the introduction to the subject by Catalina Goanta and Sofia Ranchordás, who set out to define what is an influencer, and describe the legal status and regulatory pitfalls that they face. It is, of course, difficult to delineate the subject and define the concept of “influencer” in a way that can be used for legal analysis. The word influencer itself comes from an era of celebrities, and it is intrinsically linked with advertising. However, recently there has been a rise in the social media influencer that responds to the shift in audiences from traditional media to social networks. Social media in this context is understood as a platform that allows users to upload and share content to an audience. The social media influencer is not a celebrity in the traditional sense, but she/he may carry an incredible amount of clout in his/her niche area of interest. The challenge from a regulatory perspective is that these influencers are often operating in unprofessional settings, relying on monetisation schemes that are controlled entirely by tech platforms, and where commercial endorsements are not always transparent. This is a problem because it makes deception easier, but it also gives the impression that an influencer is endorsing a product because they like it, instead of the reality that showcasing it is part of a commercial deal. Influencers have considerable power to shape trends with their audiences, and as they say, with great power comes great responsibility.
So, what is an influencer? There are a few common elements. Social media influencers operate in “word-of-mouth” advertising environments where audience trust is paramount. Influencers exert their, well, influence, in online communities through the constant production of content and through engagement with their peers. The authors identify four identifying elements of being an influencer: 1) the industry in which an influencer operates (e.g. beauty, tech, gaming, pets, kids, etc); 2) the source of influence (e.g. by already being famous as actors or sports personalities, while others are considered influencers by the number of followers in social media); 3) the reach of influence in the shape of detailed audience analytics; and 4) the legal status, namely whether an influencer operates in a corporate environment, freelances, or even acts as a consumer in more informal settings.
After providing a very thorough definition of an influencer, the authors engage in a discussion of the legal issues that surround the new influencer industry. Their central concern is with advertising. Advertising is a regulated activity in many countries, either by self-regulation, or as is the case in most of Europe, through advertising standards agencies, so the role of the influencer in the shaping of opinions, particularly in young audiences, has received the most level of scrutiny. The authors go through several examples of legal concerns that arise from the blurring of consumer/professional boundaries, particularly when it comes to endorsements, and the disclosure of whether an influencer may be praising a product in exchange for payment, or goods or services.
The authors end the chapter analyzing several other legal areas of concern. With the increased role of influencers as figures worthy of trust in their communities, the main question is one of the possible liabilities they may incur when promoting dubious products and events, such as the ill-fated Fyre Festival, defective products, or even disreputable schemes such as multi-level marketing. The book was written in 2019 and published in 2020, so the authors do not cover the recent wave of influencers promoting failed cryptocurrency schemes. Many of these influencers often fall outside of existing regulation, so it will be useful to have an area of the law dedicated solely to analysing the reach and effect of these actors, but most importantly, prepared to understand the environment in which they move. This could probably be an area of research in the future for the nascent area of Influencer Law.
The edited volume as a whole is filled with other excellent chapters. These include a notable discussion of child labor in the influencer world, by Valerie Verdoot, Mark Leiser, and Simone van der Hof. I also enjoyed and learned a lot from the chapter on the potential regulation of the influencer market using mandated disclosures, by Rossana Ducato.
I highly recommend this edited book. I enjoyed reading it cover to cover, which is rare in works of this nature. While Influencer Law may not yet be its own course or field, this book at least makes a compelling argument for the existence of a vibrant area of research.
Feb 7, 2023 Ari Waldman
Karen Levy’s book Data Driven, an incisive and accessible sociolegal study of workplace surveillance in the trucking industry, begins with a tale of superheroes. These superheroes are machines from a far-off world dedicated to saving humanity from other machines bent on our destruction. (Think “The Transformers.”) The problem is: Our would-be saviors can’t move. They’ve worked too hard for too long, saving humanity from all sorts of harm, and now, by law and by design, they must rest.
Levy, a professor in Cornell University’s Department of Information Science, tells this story, drawn directly from the pages of a trucking industry periodical, to introduce us to the electronic logging device, or ELD. ELDs are now integrated by law into every commercial truck driving across state lines. They are designed to force compliance with federal “hours-of-service” regulations, which limit the number of hours truckers can drive before taking rest breaks. Like our would-be robot saviors, trucks constrained by ELDs cannot move when their drivers have reached their hours limits. That isn’t necessarily so bad; trucker fatigue is dangerous to truckers and everyone else on the road. But, as Levy explains, ELDs are a lot more insidious.
ELDs treat the symptom, not the disease. If the symptom is fatigue, the disease is the trucking industry’s perverse economic incentives. Truckers are paid by the miles they drive, and they are paid nothing during the many hours of fueling, loading, unloading, and bathroom and meal breaks necessary to doing their jobs. (Pp. 36-48.) Plus, ELDs do more than trigger federal rest mandates. They reveal to employers when, where, and how fast a trucker is driving, when and for how long they have been resting, and when and where truckers are doing something they shouldn’t. And that is Data Driven’s central story. ELDs were sold as a new technology that would make trucking (and driving) safer. But they also enable extensive workplace surveillance by trucking companies and by the state, give management weapons to manipulate their employees, and perpetuate extractive capitalism.
Data Driven, like much of my own work, explores the gap between the law on the books—ELDs in this case—and the law on the ground, including the way the law is practiced, understood, experienced, and resisted in the real world. The book is based on years of extensive field research. Levy interviewed truckers at truck stops, read their literature, met with regulators and management, sat in on meetings, engaged with labor organizers, all while upholding ethical standards as a sociolegal researcher. Based on this work, Levy finds that ELDs are legal creations, economic tools, and cultural objects all at the same time. They help regulators enforce the law to the letter. They disrupt long-standing norms among truckers, particularly about their knowledge of the road and their independence. And they are part of a larger system of surveillance that helps firms force employee alignment with corporate goals. (P. 55.)
A particularly vivid exchange explores the last point. (P. 61.) One afternoon, starting at 12:57 PM and continuing for the next 90 minutes (sometimes at one-minute intervals!), a trucker received several messages from management: “Are you headed to delivery?” “Please call.” “What is your ETA to delivery?” “Need to start rolling.” “Why have you not called me back?” The trucker was sleeping; management didn’t care. “Why aren’t you rolling? You have hours ….” Having hours refers to additional time before legally mandated rest. Seven minutes later came the next message insisting the trucker get back on the road. Seven minutes later comes another one. The trucker responds: “Bad storm. Can’t roll now.” Three minutes later, management chimes in: “Weather Channel is showing small rain shower in your area, 1-2 inches of rain and 10 mph winds ???”
Workers, many of whom chose this grueling job specifically for its independence, now have employers looking over their shoulders. But disrupting trucking’s cultural norms is the tip of this iceberg. Having hours is a quantified metric, a decontextualized number that presumes that the only barrier to driving is the federal rest mandate. Truckers also get tired, have to use the restroom, and need to eat. “Having hours” elides all of that. Then comes the Weather Channel. Rain “in your area” says little about rain “where I am right now.” Nor does it speak to driving conditions; even light rain “in your area” can mean slippery conditions, landslide risks, and other dangers from hours of heavy rain.
ELDs, just like algorithms that purport to process large data sets try to predict people’s behavior, privilege strict and inflexible data analysis over holistic assessment and discretion. They presume that numbers tell the whole story, or at least enough of a story to make policy. As Levy shows, numbers miss the realities of trucking. (Pp. 50-51.) If you were told you had around 10 hours to complete a journey, you’d drive much more safely than if you were told you must arrive in exactly 10 hours or else. Why? The former gives you flexibility; you can drive faster when it’s safe and slower when you need to; you can factor in bathroom, meal, and rest breaks without stressing that you’re going to be a few minutes late. The latter incentivizes recklessness. The ELD turns trucking into a constant barrage of threats.
Data Driven concludes by speaking to the larger message of the ELD mandate. Faced with an epidemic of dangerous trucker fatigue, policymakers turned to surveillance and technology design rather than to addressing the underlying economic incentives that push truckers to drive while tired in the first place. (P. 153.) A richer, worker-protective solution should have been to pay truckers for their work, not for their miles driven. This would include what truckers call “detention time,” or the hours spent loading and unloading, during which they are at the mercy of dock workers and other laborers who operate on their own schedules and have radically different payment structures and incentives. But no, the neoliberal policymakers of late capitalism chose surveillance. They chose a weapon of managerial control rather than a structural change. In so doing, the trucking industry colluded with policymakers to socially construct the ELD as a tool of social control, and they made the roads more dangerous as a result.
In the end, Levy is right to warn us that truckers are the canaries in the coal mine of workplace surveillance. (P. 9.) Employers may have always kept watchful eyes on their employees, but things are worse now. Remote workplaces like trucks or home offices are no longer immune from tracking. The data—collected from inputs like wearables and social media—are more diverse. The analytics, now driven by complex algorithms, are more invasive. Modern workplace surveillance extends even beyond systems administrators knowing when you’re checking Instagram. Our bosses are watching everything; Levy’s outstanding Data Driven opens our eyes before it’s too late.
Jan 4, 2023 Daithí Mac Síthigh
“Retrofuturism” in art and literature is a look back at the (sometimes recent) past and how the stories of the future were told. The retrofuturist aesthetic can be found in present-day theme parks like Walt Disney World’s Tomorrowland and EPCOT and in the concept of steampunk. Through retrofuturism, we try to understand what was once hoped for, often as a way of understanding success or failure and of critiquing present-day efforts and priorities.
Retrofuturist impulses are particularly important in technology law scholarship. Critical appraisals of ‘smart city’ and urban innovation projects and initiatives examine how people joined the digital with the material to imagine a better world. You can’t tell the story of the smart city without at least engaging with the tales of the city. And so, in a very real and immediate way, the literature of geography, planning, and–yes–physical architecture is a key resource for the legal scholar. In The Kind of Solution a Smart City Is: Knowledge Commons and Postindustrial Pittsburgh, Michael Madison gives us a compelling retrofuturist account of Pittsburgh, the smart city. Madison’s account of a range of projects in Pittsburgh (including those of the 21st century) tells a story that is both universal and particular, tapping into the need to understand the roads taken and not taken, and what was imagined or foreseen in the recent and not so recent past.
The Governing Knowledge Commons framework, an approach to which Madison himself has made founding and abiding contributions, allows for the study of how intellectual and cultural resources (e.g. information, science, and software), as distinct from natural resources, are created and shared, and in turn governed through, by, and with communities. Applying this framework, Madison digs into the past ideas and initiatives meant to improve (or “fix”) this mid-sized Pennsylvanian city. Imagined overlapping and data-driven futures like the pioneering Pittsburgh Survey of a century ago, the 3RC (Three Rivers Connect) civic computing initiative of 1999, or reaching the final of (but not winning) the USDoT Smart City Challenge in 2014 offer rich resources for anyone seeking to understand how cities attempt to anticipate and evolve in the face of disparate and dynamic challenges.
Madison also tells the stories of the conditions for urban reform and renewal in Pittsburgh, contributing to the overall argument that context, geography and history all matter. Physical infrastructure is old. Social and political infrastructures are tied up in long-standing institutions and networks. Pittsburgh’s population has been declining. The geography of the city, in its ups, downs, rivers, and bridges, is irregular. The city’s many neighbourhoods are disconnected from political power. The City Council is not the only game in town, due to the presence of regional and other government structures. Where once there was steel, now there are universities and hospitals (“eds and meds”) and, increasingly, an innovation economy (“tech-centred development”). Yet until recently, data systems weren’t often used in municipal government.
Readers will recognize many of these facets in “post-industrial” cities around the world. Pittsburgh is one of a number of cities where ‘economic renewal efforts’ dominate cultural and political discourse, decades after the decline of a major production or extractive industry. But, as Madison makes clear, Pittsburgh is unique in how its infrastructure, population decline, geography, and changing industry are interconnected with money, power, and people, meaning that the factors and actors affecting economic renewal and the digital transition require particularly close attention.
Some political institutions and funders nowadays emphasise the “knowledge square” (e.g., as the European Commission now puts it, the interconnection between education, research, innovation, and service to society). Though Madison does not put it quite this way, his careful attention to the roles of philanthropic organisations (a distinctive part of the Pittsburgh civic story) and universities tells another dimension of Pittsburgh’s reform and development trajectory. He highlights the different roles played by the University of Pittsburgh and Carnegie Mellon University and its projects. These include the smart cities institute Metro21. Madison also traces the distinctive character of a number of major interventions, such as the Western Pennsylvania Regional Data Centre, and the individuals who have led and championed them.
This article is not (only) a celebration of a great city, though. Madison highlights the difference between the problems the city tries to solve and the biggest problems that need to be solved. He retains an appropriate scepticism about extreme smart city boosterism, calling for greater attention to evolution over creation and to the enduring role of physical infrastructure and its limits.
The dream of a better tomorrow is at the core of urban (re)imagination. As Pittsburgh moves towards being a smart city, Madison draws a contrast between the city’s “older smoky self” and its aspirations towards becoming an “equitable and forward-looking ‘green’ community”. Yet Madison has also shown us that the smoke never fully clears. Even the ‘recent’ history of what was tried and why it did or didn’t work in the late 1990s is in his account an essential part of a proper understanding of the choices now available to this particular city. Other cities will face different physical and political factors, but Madison is rightly calling on us to map and understand those local conditions; some common questions, but different answers, are what we can hope to find.
Nov 28, 2022 Stacy-Ann Elvy
Consumers accessing goods and services online are inundated with numerous disclosures, privacy policies, end user license agreements and terms and conditions. In connection with the so-called “duty to read,” consumers have historically been presumed and expected to fully review contract terms as part of the contract-making process. Yet, as several scholars have observed, consumers do not appear to consistently review contract terms: what some have called the “no-reading problem.” The failure of consumers to review and understand contract provisions before manifesting assent may incentivize companies to offer one-sided contracts with terms that are primarily beneficial to businesses.
In their new article, Contracts in the Age of Smart Readers, Professors Yonathan A. Arbel and Samuel Becher make a noteworthy contribution to scholarship in the technology and contract law fields by highlighting how nascent technological advancements in language models associated with artificial intelligence can disrupt the status quo. Their powerful article adds to an existing body of scholarship exploring the important connection between technological developments and what the authors describe as one of the underlying justifications for legal intervention in consumer transactions: the “no reading problem.”
Arbel and Becher tout various possible benefits of novel language models, which they label as “smart readers,” by offering several examples of this technology in action. They observe that armed with a smart reader app, a consumer could in theory use their smartphone to scan and receive a plain and concise explanation of boilerplate provisions in a company’s terms and conditions. Contractual text could be personalized based on the needs of each reader by factoring in cognitive, linguistic, and cultural patterns. A consumer using a smart reader could request concrete examples describing the possible implications of boilerplate clauses.
Arbel and Becher note that smart readers have the capacity to compare the terms of a company’s privacy policy with those offered by other businesses and generate an industry score that the consumer could then use to comparison shop. The authors convincingly argue that, if widely adopted, this technology could potentially enhance consumer understanding of contract terms and privacy policies and the risks associated with the same, as well as increase consumer awareness of market alternatives. They contend that smart readers may facilitate “term competition” (P. 91) in certain markets, even if the technology is not widely adopted.
After persuasively describing the potential advantages of smart readers, Arbel and Becher highlight the possible risks associated with smart readers. These concerns include the possibility of courts over-relying on consumer access to such apps, which may negatively impact outcomes for consumers. Adversarial attacks, which the authors describe as “a method of exploiting the statistical nature of machine learning models” (P. 121) may also make contractual explanations and industry scores less accurate and reliable. Arbel and Becher note that in some cases smart readers could oversimplify boilerplate terms, which could decrease consumer understanding. Lastly, businesses could offer better terms to those consumers who they believe will use smart readers and comparison shop, and less favorable terms to those who do not, thereby exacerbating discrimination concerns.
Arbel and Becher posit that legal interventions in favor of consumers are often “couched in the no-reading problem.” (P. 134.) However, smart readers offer a different way of tackling the no-reading issue. They suggest that the no-reading problem is perhaps a technological issue that smart readers can help to solve, rather than an ethical one deserving of legal intervention. The authors contend that while smart readers do not address various other justifications for pro-consumer legal intervention, such as other forms of market failure, smart readers may soon render the no-reading justification obsolete. Arbel and Becher’s notable and insightful description of smart readers’ growing potential should be of particular interest to technology law, contract law, and consumer law scholars, as well as others who are interested in learning more about the ways in which technological advancements may impact core justifications for consumer protection intervention.
Nov 2, 2022 Paul Ohm
In the aftermath of the Cambridge Analytica fiasco, Facebook was pummeled by legislators, regulators, and advocates around the globe for their poor privacy practices stemming from the way the company seemed to prioritize growth and profit over other all else. As one small part of a multipronged defense, the company hired four prominent privacy advocates, former fierce critics of the company. The early evidence suggests that these four—and other likeminded Facebook employees—haven’t had much success reorienting the company. As one data point, two years after they were hired, Frances Haugen blew the whistle on how Facebook had not done enough to weed out misinformation, combat threats to democracy, and protect vulnerable teens, again due to a relentless pursuit of growth. To be fair, the Haugen story isn’t only or primarily a privacy fiasco, but it belies the idea that good people in positions of authority have helped the fix the company from within.
This isn’t just a Facebook story. Every large technology company employs people who profess to be privacy advocates in positions of authority, yet their collective efforts do not seem to have had done much to alter the troubling trajectory of their employers’ products and services. Ari Waldman, the deeply interdisciplinary privacy law scholar from Northeastern University, has written a vital and important book investigating why bad privacy outcomes occur at firms that employ well-meaning and well-trained privacy professionals. Drawn from dozens of interviews with software engineers and privacy professionals from many technology companies, Waldman presents a compelling and distressing picture, revealing the way companies constrain the influence of privacy-focused employees, repurposing their work toward serving data extractive goals, eventually redefining privacy law itself in narrow, compliance-focused terms.
A trained sociologist and legal scholar, Waldman conducted 125 interviews over four years and insinuated himself into product design meetings, industry conferences, and company breakrooms, revealing a rigorous and detailed description of the way privacy is subverted and denied inside these companies. The work builds on and pays due credit to the groundbreaking qualitative work of Deirdre Mulligan and Ken Bamberger, the famous “privacy on the ground” study from a decade ago, even as Waldman offers a respectful corrective, pushing back on many of the sunnier conclusions of the earlier work.
Waldman’s conclusions are layered and sophisticated and hard to do justice to in a short review. Technology companies deploy a “coercive bureaucracy,” multiple strategies designed to limit privacy reforms and to disempower privacy professionals. One key mechanism of the coercive bureaucracy is “managerialism”, borrowing from Julie Cohen (who in turn borrowed from Judith Resnik and others), meaning the cynical transmutation of laws like the GDPR and CCPA from obligations designed to protect consumers into narrow compliance measures focused on limiting liability and deflecting regulator attention, in some cases essentially inverting these laws to require nothing that might impede the company’s growth and revenue goals.
Managerialism is but one tool of the coercive bureaucracy, and Waldman identifies too many others to list comprehensively, but to highlight a few: privacy gets redefined to being about giving users control over their personal information. (Chapter 2 is an amazing primer of the vast literature making this argument.) Privacy gets translated into narrow, codeable targets, such as finding new places to apply encryption. Privacy is what you outsource to growing armies of GDPR and CCPA consultants.
Although Waldman has written a book for scholars, it will also prove useful to privacy professionals who might recognize the disconnect between the hard work they are doing and the poor privacy outcomes their companies are producing. Chapters 5 and 6 read like how-to guides for stuck privacy professionals, building from the micro to the macro. At the individual level, Waldman surveys the subtle, small “traps” that companies use to constrain the influence of their workers, such as the “expertise trap,” which siloes people into narrow lanes of expertise, or the “access trap,” the belief that advocates should choose their battles rather than complain about every privacy transgression lest they be cut out of the decisionmaking loop. Waldman’s book will help those living inside a coercive bureaucracy spot, and maybe resist, the mechanisms constraining their work.
Ultimately, Waldman does not believe that individual awareness and resistance will be enough. Chapter 6 is a broad call to action, if not revolution, to recruit privacy professionals into a new movement, one that might serve as a “counterweight to corporate power,” the chapter’s oft-repeated mantra. He outlines fixes for privacy discourse, privacy law, and privacy organizing, to help us find new ways to break coercive bureaucracies. He makes several explicit calls to the labor movement, at one point calling for the formation of a new union of privacy workers.
There is so much I like (lots!) about this book. It provides deep, rich, and rigorously gathered empirical data about the forces that keep privacy at bay inside technology companies. It synthesizes these observations into compelling explorations of the mechanisms at play. It engages deeply and efficiently with multiple vast literatures, making it a readable and concise recommendation for newcomers to the field. I have recommended Chapter 2 to anybody still under the thrall of the consent-and-control model of privacy law; Chapter 3 to the staff working for state regulators drafting privacy rules; and the entire book to those trying to operationalize Julie Cohen’s theories. It offers multiple concrete prescriptions on how we might do better, ranging from the narrowly practical to the audaciously ambitious. It does all of this in crystal clear prose, studded with quotes and conversations from the empirical work, and suffused throughout with the considerable humanity of the author. It’s a welcome and rightful new inductee into the canon of privacy law, a must-read for students, scholars, policymakers, and privacy professionals.
Oct 26, 2022 Rebecca Crootof
I always love scholarship that forces me to pause and question my baseline assumptions. And so—as someone who has written of the need to close accountability gaps associated with malicious cyberoperations, IoT devices, and autonomous weapon systems—I was delighted to read John Danaher’s Tragic Choices and the Virtue of Techno-Responsibility Gaps. In this work, Danaher challenges everyone who has ever argued that new technologies problematically undermine traditional accountability structures by quietly observing that these new gaps are…maybe sometimes a good thing?
While Danaher tends to focus more on moral responsibility than legal liability, if you are a techlaw scholar thinking about accountability gaps in any context, add this to your reading list. Danaher writes in a relaxed and engaging style, includes a fantastic literature review of non-legal texts on accountability gaps, and explores a counterintuitive argument—all in a piece that clocks in at a svelte 22 pages of text. (Would that I could accomplish so much, so smoothly, in so few words!)
Danaher defines a “Techno-Responsibility Gap” as follows: “As machines grow in their autonomous power (i.e. their ability to do things independently of human control or direction), they are likely to be causally responsible for positive and negative outcomes in the world. However, due to their properties, these machines cannot, or will not, be morally or legally responsible for these outcomes. This gives rise to a potential responsibility gap: where once it may have been possible to attribute these outcomes to a responsible agent, it no longer will be.” Danaher then distinguishes the various forward- and backward-looking forms techno-responsibility gaps might take. There are (1) accountability gaps, which exist when there’s no one to provide a public account for the harm; (2) culpability gaps, which exist when there’s no one to take the blame; (3) compensation gaps, which exist when there’s no one to pay for the harm; (4) obligation gaps, which exist when there’s no one who ensures the harm is avoided; and (5) virtue gaps, which exist when no one takes responsibility for the harmful acts. Danaher also notes the distinction between positive responsibility (“Great job there!”) and negative responsibility (“Why didn’t you . . .?!”).
Danaher then summarizes familiar proposed means of eliminating these gaps, most of which boil down to justifications for ascribing accountability to a prescribed human or non-human entity. He concludes that, for all of the disagreement around how best to address them, “most contributors to the techno-responsibility gap debate tend to agree on one thing: the creation of techno-responsibility gaps is a problem.” Why? Because responsibility is always a good thing. Right? Right?
Maybe not! To set up his argument for why we might sometimes want to prioritize other goals over ensuring accountability, Danaher starts with the problem of tragic choices. Human decision-makers often confront questions where moral considerations simultaneously weigh in favor of different answers and it is difficult or even impossible to reach a morally comfortable conclusion. We all face these choices in our daily lives. (Do I give my discretionary funds to this or that charity?) But they become policy questions when we need to determine how best to allocate scarce resources (Should hospitals privilege this or that type of patient when deciding who receives a needed ventilator?) or weigh costs to X against costs to Y (How to balance a right to speech against a right not to be threatened?).
When confronted with these tragic choices, we—as individuals, as institutions, or as societies—may handle the moral difficulty of reaching a conclusion in various ways. First, we might delude ourselves into believing it’s actually an easy question (“illusionism”). This can manifest in ignoring costs, compartmentalizing them, or rationalizing them away. Second, we might delegate the choice to another (“delegation”). We do this when we ask waitstaff what we should order, look to a panel of judges to decide the scope of a law, or flip a coin to determine our next course of action. Third, we might make a decision and bear the psychological costs ourselves (“responsibilization”).
One of Danaher’s main points is that none of these responses will always be better or worse than the others. Rather, in a classically lawyerly move, Danaher maintains that the preferable response will depend on the situation and context. Despite our collective bias towards responsibilization, each of these responses has distinct benefits and drawbacks.
Illusionism permits mental comfort at the expense of honesty. Delegation allows for shifting the psychological and moral costs to a (possibly more informed, capable, or impartial) substitute actor. But it also risks a concentrated group or institution bearing these costs, transference to an inept decision-maker and consequently poor outcomes, and the failure of the original actor to develop or maintain decision-making skills. Finally, responsibilization enables moral agency and all sorts of accountability—but does so at the possible cost of unjustly transforming decision-makers into scapegoats. (This point reminded me of an argument against including steering wheels in fully autonomous vehicles: the idea was that, in the event of a deadly crash, the human operator would unfairly blame themselves for not intervening despite not being able to act with the reflexes necessary to prevent the accident.)
If each response to a tech-fostered accountability gap has distinct pros and cons, there will necessarily be situations where delegation will be preferable to responsibilization. Further, Danaher argues, the possibility of delegating to an algorithm, rather than to another human, may change the balance of benefits and harms associated with these different responses, insofar as it eliminates the delegation drawback of concentrating the psychological and moral costs of tragic choices with few individuals. To take advantage of this reduced cost on human decision-makers, Danaher concludes, we must be willing to live with some techno-responsibility gaps.
Danaher suggests that human online content moderators provide an example of when this tradeoff might be worthwhile. These decision-makers have a stressful, difficult job; they save untold numbers of platform users from having to view offensive and traumatizing content, but they do so at great psychological expense. Assuming both human and algorithms performed moderation tasks equally well, transferring content moderation decision-making power to an algorithm would minimize harm to humans. Similar arguments could be made for drone operators, police body-cam reviewers, and any other human charged with sifting through painful content to determine what can be cleared for public release.
Danaher is quick to qualify his argument. To the extent they are made, delegations should be made carefully; his analysis does not suggest that we should always delegate decisions to machine systems. And the fact that algorithmic decision-makers reduce some of the costs of delegation does not mean they eliminate other costs; there are still plenty of reasons to be wary of accountability gaps. Danaher also engages, in a wonderfully non-defensive manner, with various alternative versions and critiques of his argument. He explores a proposal to employ randomization as a low-cost form of algorithmic delegation, the concern that delegation fosters agency-laundering and liability evasion, and a query as to when we might (and might not) want to make the costs and tradeoffs inherent in tragic choices more explicit.
This thoughtful, dense, yet accessible piece invites readers to question our assumptions about why we assume accountability—and, specifically, responsibilization—is always preferable to the alternatives. I will likely to continue to argue for closing tech-fostered accountability gaps, but thanks to this piece, my arguments will now be far more nuanced.
