Faculty Bibliography
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The Research Handbook on Health, AI and the Law explores the use of AI in healthcare, identifying the important laws and ethical issues that arise from its use. Adopting an international approach, it analyses the varying responses of multiple jurisdictions to the use of AI and examines the influence of major religious and secular ethical traditions.
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For years, universities have been less inclined to protect speech and quicker to sanction it. After this spring’s protests, it will be difficult to turn back.
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Yabancı hukuka yargısal atıf yapılmasına ilişkin tartışmalarda hâkimler değerleri tartışmaktadır. Ancak çoğu zaman tartıştıkları değerleri kabul etmemektedirler veya çoğunluk ve muhalif görüşlerde neden bir değeri diğerine tercih ettiklerine dair özel gerekçeler sunmamaktadırlar, bunun yerine genel bir iddiada bulunmak için olumsuz yabancı hukuk modellerini benimsemeyi tercih ediyorlar. Bu fenomenin bir örneği, keyfiliğe atıfta bulunmak için "kadijustiz" kelimesinin (Max Weber tarafından ortaya atılan ve Yargıç Felix Frankfurter tarafından 1949'da alınan bir kararla yaygınlaştırılan bir terim) Amerikan yargısal atfıdır. Ancak bu uygulama iki nedenden dolayı yanlıştır. Birincisi, İslam hukuk tarihçilerinin Orta Çağ'dan erken modern dönemlere kadar Memlük, Osmanlı ve diğer mahkemelerdeki İslami yargı prosedürlerini ayrıntılı olarak incelerken uzun zamandır işaret ettiği gibi, bu doğru değildir. İkincisi, kadijustiz'e yargısal atıf, tartışmalı yargısal karar alma süreçlerinde belirli değerlerin diğerlerine göre benimsenmesinin nedenlerini gizlemekte ve böylece, buna atıf yapan hakimlerin argümanlarını genel olarak zayıflatmaktadır.
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In a Time interview, Trump tells us how law, order and freedom are at stake in November.
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The Constitution gives the president no “duties” over presidential elections; his actions are therefore personal, and not immune.
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We ask whether increased public scrutiny leads to the more effective use of predictive algorithms. We focus on the context of bail, where judges face heightened public scrutiny during competitive partisan elections. We find that judges up for reelection are much more likely to follow the algorithmic recommendation to detain high-risk defendants just before an election. However, release decisions return to normal shortly after the election, and there is little change in pretrial misconduct rates, indicating that heightened public scrutiny, at least through competitive partisan elections, will not lead to the more effective use of predictive algorithms in bail.
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We investigate whether removing a previously-obtained criminal record improves employment outcomes. We estimate the causal impact of criminal record remediation laws that have been widely enacted with the goal of improving employment opportunities for millions of individuals with records. We find consistent evidence that removing an existing record does not improve labor market outcomes, on average. A notable exception is participation in gig work through online platforms, which often screen workers based on their records but not their employment histories. The evidence is consistent with records initially scarring labor market trajectories in a way that is difficult to undo later.
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The editors of the Harvard Law Review respectfully dedicate this issue to Justice Sandra Day O’Connor.
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Artificial intelligence (AI)-enabled chatbots are increasingly being used to help people manage their mental health. Chatbots for mental health and particularly ‘wellness’ applications currently exist in a regulatory ‘gray area’. Indeed, most generative AI-powered wellness apps will not be reviewed by health regulators. However, recent findings suggest that users of these apps sometimes use them to share mental health problems and even to seek support during crises, and that the apps sometimes respond in a manner that increases the risk of harm to the user, a challenge that the current US regulatory structure is not well equipped to address. In this Perspective, we discuss the regulatory landscape and potential health risks of AI-enabled wellness apps. Although we focus on the United States, there are similar challenges for regulators across the globe. We discuss the problems that arise when AI-based wellness apps cross into medical territory and the implications for app developers and regulatory bodies, and we outline outstanding priorities for the field.
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In arguments about Presidential immunity, the conservative Justices, who avoided mentioning Trump, made clear that they are less concerned with holding him accountable than with shielding former Presidents from retribution.
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In Federalist No. 78, Alexander Hamilton argued for locating interpretive authority over law separately from those institutions tasked with formulating it. Hamilton’s vision, never accurate as a description of American practice, has not been credible for a long time. To the extent massive power is still allocated to judges, our legal institutions have been out of step with our legal theory, which has long regarded them as political actors and policymakers. More practically, every Term it is clearer and clearer that the role of the Supreme Court in statutory cases (including checking administrative rule-making and other processes) is, if anything, more menacing than its role in the rare instances when it deploys its heaviest weaponry of constitutional invalidation. Against progressive calls to reclaim the judiciary, this Article completes our proposal to disempower courts exercising lawmaking authority—including when they are interpreting statutes alone. Indeed, the same considerations that counsel the constitutional disempowerment of courts counsel their statutory disempowerment, and the allocation or reallocation of their authority over law to politically accountable agents. The heart of our Article offers a survey of court disempowerment strategies and tools, which are comparable to though not identical with the disempowerment mechanisms that have been proposed in the arena of constitutional reform. Such strategies and tools are appealing in the short term; but in the long term, a fuller rethinking our desirable institutional plan of legal interpretation beckons. Available and existing disempowerment strategies for courts are best conceived as early and partial versions of full-scale allocation of interpretive authority over law to “political” branches and openly political control.
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From at least the early twentieth century, legal scholars have recognized that rights and other legal relations inhere between individual legal actors, forming a vast and complex social network. Yet, no legal scholar has used the mathematical machinery of network theory to formalize these relationships. Here, we propose the first such approach by modelling a rudimentary, static set of real property relations using network theory. Then, we apply our toy model to measure the level of modularity—essentially, the community structure—among aggregations of these real property relations and associated actors. In so doing, we show that even for a very basic set of relations and actors, law may employ modular structures to manage complexity. Property, torts, contracts, intellectual property, and other areas of the law arguably reduce information costs in similar, quantifiable ways by chopping up the world of interactions between parties into manageable modules that are semi-autonomous. We also posit that our network science approach to jurisprudential issues can be adapted to quantify many other important aspects of legal systems. This article is part of the theme issue 'A complexity science approach to law and governance'.
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A crucial path to legal status for immigrant victims of crimes is the U visa, which Congress established with strong bipartisan support to protect victims of particular crimes who are helpful to law enforcement. Because the U visa was intended to encourage reporting of crimes, the application requires a certification form to be completed by a federal, state, or local authority that is investigating or prosecuting the alleged offense. Arbitrary and inconsistent certification decisions by state and local authorities make it especially important to identify relevant federal authorities that can serve as certifying authorities for U visas. This Piece argues that congressional committees and subcommittees that engage in investigations qualify as certifying authorities under the statute and regulations. To date, these congressional committees have never certified a U visa. The Piece provides three examples of congressional investigations in which U visa certification would be warranted: investigations into medical abuses of detained women, the so-called “Zero Tolerance” family-separation policy, and the use of solitary confinement in immigration detention.
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In a historical moment defined by massive economic and political inequality, legal scholars are exploring ways that law can contribute to the project of building a more equal society. Central to this effort is the attempt to design laws that enable the poor and working class to organize and build power with which they can countervail the influence of corporations and the wealthy. Previous work has identified ways in which law can, in fact, enable social-movement organizing by poor and working-class people. But there’s a problem. Enacting laws to facilitate social-movement organizing requires social movements already powerful enough to secure enactment of those laws. Hence, a chicken-and-egg dilemma plagues the relationship between law and organizing: power- building laws may be needed to facilitate social-movement growth, but social-movement growth seems a prerequisite to enactment of power- building laws. This Essay examines the chicken-and-egg puzzle and then offers three potential solutions. By engaging in disruption, shifting political jurisdictions, and shifting from one branch of government to another, organizations of poor and working-class people can enact laws to enable the construction of countervailing power.
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Health care delivery is shifting away from the clinic and into the home. Even prior to the COVID-19 pandemic, the use of telehealth, wearable sensors, ambient surveillance, and other products was on the rise. In the coming years, patients will increasingly interact with digital products at every stage of their care, such as using wearable sensors to monitor changes in temperature or blood pressure, conducting self-directed testing before virtually meeting with a physician for a diagnosis, and using smart pills to document their adherence to prescribed treatments. This volume reflects on the explosion of at-home digital health care and explores the ethical, legal, regulatory, and reimbursement impacts of this shift away from the 20th-century focus on clinics and hospitals towards a more modern health care model. This title is also available as Open Access on Cambridge Core.
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In his first interview after the release of his controversial report, the former special counsel insists that it was not his job to write for the public.
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The use of Artificial Intelligence (AI) based on data-driven algorithms has become ubiquitous in today's society. Yet, in many cases and especially when stakes are high, humans still make final decisions. The critical question, therefore, is whether AI helps humans make better decisions as compared to a human alone or AI an alone. We introduce a new methodological framework that can be used to answer experimentally this question with no additional assumptions. We measure a decision maker's ability to make correct decisions using standard classification metrics based on the baseline potential outcome. We consider a single-blinded experimental design, in which the provision of AI-generated recommendations is randomized across cases with a human making final decisions. Under this experimental design, we show how to compare the performance of three alternative decision-making systems--human-alone, human-with-AI, and AI-alone. We apply the proposed methodology to the data from our own randomized controlled trial of a pretrial risk assessment instrument. We find that AI recommendations do not improve the classification accuracy of a judge's decision to impose cash bail. Our analysis also shows that AI-alone decisions generally perform worse than human decisions with or without AI assistance. Finally, AI recommendations tend to impose cash bail on non-white arrestees more often than necessary when compared to white arrestees.
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Eliminating the line could help ensure that voters, not party insiders, have the final say.
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Guhan Subramanian & Michael Klausner, Deals: The Economic Structure of Business Transactions (2024).
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Instant-runoff voting (IRV) is having a moment. More than a dozen American localities have adopted it over the last few years. So have two states. Up to four more states may vote on switching to IRV in the 2024 election. In light of this momentum, it’s imperative to know how well IRV performs in practice. In particular, how often does IRV elect the candidate whom a majority of voters prefer over every other candidate in a head-to-head matchup, that is, the Condorcet winner? To answer this question, this article both surveys the existing literature on American IRV elections and analyzes a new dataset of almost two hundred foreign IRV races. Both approaches lead to the same conclusion: In actual elections—as opposed to in arithmetical examples or in simulated races—IRV almost always elects the Condorcet winner. What’s more, a Condorcet winner almost always exists. These findings help allay the concern that candidates lacking majority support frequently prevail under IRV. The results also reveal an electorate more rational than many might think: voters whose preferences among candidates are, at least, coherent in virtually all cases.
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In this paper, we examine the primary impact of two categories of food recovery policies on food donation and the secondary impact on food safety, food waste, and food insecurity in U.S. states. As one method of food recovery, food donation can reduce food waste while mitigating food insecurity, and it can be promoted in U.S. states through strong liability protection policies that provide legal protection to food donors and through tax incentivization policies that financially reward food donors via deductions and/or credits. To provide an initial evaluation of the effects of these policies, we coded each state’s food recovery policies in 2012 and 2018 and compared strong policies versus weak policies. Using data from multiple sources, we found that states with stronger liability protection policies had more food donations, and states that provide tax incentivization had more food waste. Although our analyses were correlational, rather than causal, and were reliant upon limited data, our results demonstrate that the current food recovery policy landscape in U.S. states does relate to important food waste outcomes. We discuss the implications of these findings for crafting more effective policies that encourage food recovery.
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The clock is running out on Trump in the New York election interference case.
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This JAMA Forum discusses the legacies of slavery, efforts underway at colleges and universities to explore and address the legacies of slavery, and health care system actions to address structural racism.
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A requiem for Section 3 of the Fourteenth Amendment.
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This Viewpoint discusses the Alabama Supreme Court’s opinion on in vitro fertilization and how it plays into a larger push for fetal and embryonic personhood.
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Scholarship on the phenomena of big data and algorithmically-driven digital environments has largely studied these technological and economic phenomena as monolithic practices, with little interest in the varied quality of contributions by data subjects and data processors. Taking a pragmatic, industry-inspired approach to measuring the quality of contributions, this work finds evidence for a wide range of relative value contributions by data subjects. In some cases, a very small proportion of data from a few data subjects is sufficient to achieve the same performance on a given task as would be achieved with a much larger data set. Likewise, algorithmic models generated by different data processors for the same task and with the same data resources show a wide range in quality of contribution, even in highly performance-incentivized conditions. In short, contrary to the trope of data as the new oil, data subjects, and indeed individual data points within the same data set, are neither equal nor fungible. Moreover, the role of talent and skill in algorithmic development is significant, as with other forms of innovation. Both of these observations have received little, if any, attention in discussions of data governance. In this essay, I present evidence that both data subjects and data controllers exhibit significant variations in the measured value of their contributions to the standard Big Data pipeline. I then establish that such variations are worth considering in technology policy for privacy, competition, and innovation. The observation of substantial variation among data subjects and data processors could be important in crafting appropriate law for the Big Data economy. Heterogeneity in value contribution is undertheorized in tech law scholarship and implications for privacy law, competition policy, and innovation. The work concludes by highlighting some of these implications and posing an empirical research agenda to fill in information needed to realize policies sensitive to the wide range of talent and skill exhibited by data subjects and data processors alike.