Law Commons^™

Regulating Machine Learning: The Challenge Of Heterogeneity, Cary Coglianese

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Machine learning, or artificial intelligence, refers to a vast array of different algorithms that are being put to highly varied uses, including in transportation, medicine, social media, marketing, and many other settings. Not only do machine-learning algorithms vary widely across their types and uses, but they are evolving constantly. Even the same algorithm can perform quite differently over time as it is fed new data. Due to the staggering heterogeneity of these algorithms, multiple regulatory agencies will be needed to regulate the use of machine learning, each within their own discrete area of specialization. Even these specialized expert agencies, though, …

Assessing Automated Administration, Cary Coglianese, Alicia Lai

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To fulfill their responsibilities, governments rely on administrators and employees who, simply because they are human, are prone to individual and group decision-making errors. These errors have at times produced both major tragedies and minor inefficiencies. One potential strategy for overcoming cognitive limitations and group fallibilities is to invest in artificial intelligence (AI) tools that allow for the automation of governmental tasks, thereby reducing reliance on human decision-making. Yet as much as AI tools show promise for improving public administration, automation itself can fail or can generate controversy. Public administrators face the question of when exactly they should use automation. …

Moving Toward Personalized Law, Cary Coglianese

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Rules operate as a tool of governance by making generalizations, thereby cutting down on government officials’ need to make individual determinations. But because they are generalizations, rules can result in inefficient or perverse outcomes due to their over- and under-inclusiveness. With the aid of advances in machine-learning algorithms, however, it is becoming increasingly possible to imagine governments shifting away from a predominant reliance on general rules and instead moving toward increased reliance on precise individual determinations—or on “personalized law,” to use the term Omri Ben-Shahar and Ariel Porat use in the title of their 2021 book. Among the various technological, …

Algorithm Vs. Algorithm, Cary Coglianese, Alicia Lai

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Critics raise alarm bells about governmental use of digital algorithms, charging that they are too complex, inscrutable, and prone to bias. A realistic assessment of digital algorithms, though, must acknowledge that government is already driven by algorithms of arguably greater complexity and potential for abuse: the algorithms implicit in human decision-making. The human brain operates algorithmically through complex neural networks. And when humans make collective decisions, they operate via algorithms too—those reflected in legislative, judicial, and administrative processes. Yet these human algorithms undeniably fail and are far from transparent. On an individual level, human decision-making suffers from memory limitations, fatigue, …

From Negative To Positive Algorithm Rights, Cary Coglianese, Kat Hefter

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Artificial intelligence, or “AI,” is raising alarm bells. Advocates and scholars propose policies to constrain or even prohibit certain AI uses by governmental entities. These efforts to establish a negative right to be free from AI stem from an understandable motivation to protect the public from arbitrary, biased, or unjust applications of algorithms. This movement to enshrine protective rights follows a familiar pattern of suspicion that has accompanied the introduction of other technologies into governmental processes. Sometimes this initial suspicion of a new technology later transforms into widespread acceptance and even a demand for its use. In this paper, we …

Antitrust By Algorithm, Cary Coglianese, Alicia Lai

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Technological innovation is changing private markets around the world. New advances in digital technology have created new opportunities for subtle and evasive forms of anticompetitive behavior by private firms. But some of these same technological advances could also help antitrust regulators improve their performance in detecting and responding to unlawful private conduct. We foresee that the growing digital complexity of the marketplace will necessitate that antitrust authorities increasingly rely on machine-learning algorithms to oversee market behavior. In making this transition, authorities will need to meet several key institutional challenges—building organizational capacity, avoiding legal pitfalls, and establishing public trust—to ensure successful …

Regulating New Tech: Problems, Pathways, And People, Cary Coglianese

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New technologies bring with them many promises, but also a series of new problems. Even though these problems are new, they are not unlike the types of problems that regulators have long addressed in other contexts. The lessons from regulation in the past can thus guide regulatory efforts today. Regulators must focus on understanding the problems they seek to address and the causal pathways that lead to these problems. Then they must undertake efforts to shape the behavior of those in industry so that private sector managers focus on their technologies’ problems and take actions to interrupt the causal pathways. …

What Is The Relationship Between Language And Thought?: Linguistic Relativity And Its Implications For Copyright, Christopher S. Yoo

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To date, copyright scholarship has almost completely overlooked the linguistics and cognitive psychology literature exploring the connection between language and thought. An exploration of the two major strains of this literature, known as universal grammar (associated with Noam Chomsky) and linguistic relativity (centered around the Sapir-Whorf hypothesis), offers insights into the copyrightability of constructed languages and of the type of software packages at issue in Google v. Oracle recently decided by the Supreme Court. It turns to modularity theory as the key idea unifying the analysis of both languages and software in ways that suggest that the information filtering associated …

Climate And Transportation Policy Sequencing In California And Quebec, Sonya Ziaja, Mark Purdon, Julie Witcover, Colin Murphy, Mark Winfield, Genevieve Giuliano, Charles Séguin, Colleen Kaiser, Jacques Papy, Lewis Fulton

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We compare flexible low-carbon regulations in the transportation sector and their interaction and sequencing with greenhouse gas emissions trading systems in California and Quebec. As momentum builds for greater climate action, it is necessary to better understand how carbon markets and other low-carbon transportation policies influence one another. First, we demonstrate that emissions trading between California and Quebec has been asymmetric, with linking having little influence on carbon prices from California's perspective but leading to a considerable cost reduction from the point of view of Quebec. Second, we present evidence that Quebec has replicated many of California's low-carbon transportation policies …

Power And Statistical Significance In Securities Fraud Litigation, Jill E. Fisch, Jonah B. Gelbach

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Event studies, a half-century-old approach to measuring the effect of events on stock prices, are now ubiquitous in securities fraud litigation. In determining whether the event study demonstrates a price effect, expert witnesses typically base their conclusion on whether the results are statistically significant at the 95% confidence level, a threshold that is drawn from the academic literature. As a positive matter, this represents a disconnect with legal standards of proof. As a normative matter, it may reduce enforcement of fraud claims because litigation event studies typically involve quite low statistical power even for large-scale frauds.

This paper, written for …

Administrative Law In The Automated State, Cary Coglianese

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In the future, administrative agencies will rely increasingly on digital automation powered by machine learning algorithms. Can U.S. administrative law accommodate such a future? Not only might a highly automated state readily meet longstanding administrative law principles, but the responsible use of machine learning algorithms might perform even better than the status quo in terms of fulfilling administrative law’s core values of expert decision-making and democratic accountability. Algorithmic governance clearly promises more accurate, data-driven decisions. Moreover, due to their mathematical properties, algorithms might well prove to be more faithful agents of democratic institutions. Yet even if an automated state were …

Contracting For Algorithmic Accountability, Cary Coglianese, Erik Lampmann

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As local, state, and federal governments increase their reliance on artificial intelligence (AI) decision-making tools designed and operated by private contractors, so too do public concerns increase over the accountability and transparency of such AI tools. But current calls to respond to these concerns by banning governments from using AI will only deny society the benefits that prudent use of such technology can provide. In this Article, we argue that government agencies should pursue a more nuanced and effective approach to governing the governmental use of AI by structuring their procurement contracts for AI tools and services in ways that …

On Environmental, Climate Change & National Security Law, Mark P. Nevitt

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This Article offers a new way to think about climate change. Two new climate change assessments — the 2018 Fourth National Climate Assessment (NCA) and the United Nations Intergovernmental Panel’s Special Report on Climate Change — prominently highlight climate change’s multifaceted national security risks. Indeed, not only is climate change a “super wicked” environmental problem, it also accelerates existing national security threats, acting as both a “threat accelerant” and “catalyst for conflict.” Further, climate change increases the intensity and frequency of extreme weather events while threatening nations’ territorial integrity and sovereignty through rising sea levels. It causes both internal displacement …

Environmental Soft Law As A Governance Strategy, Cary Coglianese

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Soft law governance relies on nongovernmental institutions that establish and implement voluntary standards. Compared with traditional hard law solutions to societal and economic problems, soft law alternatives promise to be more politically feasible to establish and then easier to adapt in the face of changing circumstances. They may also seem more likely to be flexible in what they demand of targeted businesses and other entities. But can soft law actually work to solve major problems? This Article considers the value of soft law governance through the lens of three major voluntary, nongovernmental initiatives that address environmental concerns: (1) ISO 14001 …

Deploying Machine Learning For A Sustainable Future, Cary Coglianese

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To meet the environmental challenges of a warming planet and an increasingly complex, high tech economy, government must become smarter about how it makes policies and deploys its limited resources. It specifically needs to build a robust capacity to analyze large volumes of environmental and economic data by using machine-learning algorithms to improve regulatory oversight, monitoring, and decision-making. Three challenges can be expected to drive the need for algorithmic environmental governance: more problems, less funding, and growing public demands. This paper explains why algorithmic governance will prove pivotal in meeting these challenges, but it also presents four likely obstacles that …

Regulation Of Algorithmic Tools In The United States, Christopher S. Yoo, Alicia Lai

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Policymakers in the United States have just begun to address regulation of artificial intelligence technologies in recent years, gaining momentum through calls for additional research funding, piece-meal guidance, proposals, and legislation at all levels of government. This Article provides an overview of high-level federal initiatives for general artificial intelligence (AI) applications set forth by the U.S. president and responding agencies, early indications from the incoming Biden Administration, targeted federal initiatives for sector-specific AI applications, pending federal legislative proposals, and state and local initiatives. The regulation of the algorithmic ecosystem will continue to evolve as the United States continues to search …

The Ai Author In Litigation, Yvette Joy Liebesman, Julie Cromer Young

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Many scholars have posited whether a computer possessing Artificial Intelligence (AI) could be considered an author as defined per the Copyright Act of 1976. What was once a thought experiment is now becoming reality. To date, scholarship has focused primarily been on whether an AI meets the requirements of authorship from a purely objective legal framework or whether an AI could be an author based on the doctrines of incentives, independent creation, and creativity.

However, a burden inherent in the rights and liabilities of authorship is the ability to be held liable if that author’s expressive work is infringing on …

Transactional Scripts In Contract Stacks, Shaanan Cohney, David A. Hoffman

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Deals accomplished through software persistently residing on computer networks—sometimes called smart contracts, but better termed transactional scripts—embody a potentially revolutionary contracting innovation. Ours is the first precise account in the legal literature of how such scripts are created, and when they produce errors of legal significance.

Scripts’ most celebrated use case is for transactions operating exclusively on public, permissionless, blockchains: such exchanges eliminate the need for trusted intermediaries and seem to permit parties to commit ex ante to automated performance. But public transactional scripts are costly both to develop and execute, with significant fees imposed for data storage. Worse, bugs …

Decarbonization In Democracy, Shelley Welton

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Conventional wisdom holds that democracy is structurally ill-equipped to confront climate change. As the story goes, because each of us tends to dismiss consequences that befall people in other places and in future times, “the people” cannot be trusted to craft adequate decarbonization policies, designed to reduce present-day, domestic carbon emissions. Accordingly, U.S. climate change policy has focused on technocratic fixes that operate predominantly through executive action to escape democratic politics — with vanishingly little to show for it after a change in presidential administration. To help craft a more durable U.S. climate change strategy, this Article scrutinizes the purported …

Making Sustainability Disclosure Sustainable, Jill E. Fisch

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Sustainability is receiving increasing attention from issuers, investors and regulators. The desire to understand issuer sustainability practices and their relationship to economic performance has resulted in a proliferation of sustainability disclosure regimes and standards. The range of approaches to disclosure, however, limit the comparability and reliability of the information disclosed. The Securities & Exchange Commission (SEC) has solicited comment on whether to require expanded sustainability disclosures in issuer’s periodic financial reporting, and investors have communicated broad-based support for such expanded disclosures, but, to date, the SEC has not required general sustainability disclosure.

This Article argues that claims about the relationship …

Clean Energy Justice: Charting An Emerging Agenda, Shelley Welton, Joel B. Eisen

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The rapid transition to clean energy is fraught with potential inequities. As clean energy policies ramp up in scale and ambition, they confront challenging new questions: Who should pay for the transition? Who should live next to the industrial-scale wind and solar farms these policies promote? Will the new “green” economy be a fairer one, with more widespread opportunity, than the fossil fuel economy it is replacing? Who gets to decide what kinds of resources power our decarbonized world? In this article, we assert that it is useful to understand these challenges collectively, as part of an emerging agenda of …

Transparency And Algorithmic Governance, Cary Coglianese, David Lehr

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Machine-learning algorithms are improving and automating important functions in medicine, transportation, and business. Government officials have also started to take notice of the accuracy and speed that such algorithms provide, increasingly relying on them to aid with consequential public-sector functions, including tax administration, regulatory oversight, and benefits administration. Despite machine-learning algorithms’ superior predictive power over conventional analytic tools, algorithmic forecasts are difficult to understand and explain. Machine learning’s “black-box” nature has thus raised concern: Can algorithmic governance be squared with legal principles of governmental transparency? We analyze this question and conclude that machine-learning algorithms’ relative inscrutability does not pose a …

Paul Baran, Network Theory, And The Past, Present, And Future Of Internet, Christopher S. Yoo

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Paul Baran’s seminal 1964 article “On Distributed Communications Networks” that first proposed packet switching also advanced an underappreciated vision of network architecture: a lattice-like, distributed network, in which each node of the Internet would be homogeneous and equal in status to all other nodes. Scholars who have subsequently embraced the concept of a lattice-like network approach have largely overlooked the extent to which it is both inconsistent with network theory (associated with the work of Duncan Watts and Albert-László Barabási), which emphasizes the importance of short cuts and hubs in enabling networks to scale, and the actual way, the Internet …

Lowering Legal Barriers To Rpki Adoption, Christopher S. Yoo, David A. Wishnick

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Across the Internet, mistaken and malicious routing announcements impose significant costs on users and network operators. To make routing announcements more reliable and secure, Internet coordination bodies have encouraged network operators to adopt the Resource Public Key Infrastructure (“RPKI”) framework. Despite this encouragement, RPKI’s adoption rates are low, especially in North America.

This report presents the results of a year-long investigation into the hypothesis—widespread within the network operator community—that legal issues pose barriers to RPKI adoption and are one cause of the disparities between North America and other regions of the world. On the basis of interviews and analysis of …

Tactful Inattention: Erving Goffman, Privacy In The Digital Age, And The Virtue Of Averting One's Eyes, Elizabeth De Armond

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No abstract provided.

How Much Should We Spend To Protect Privacy?: Data Breaches And The Need For Information We Do Not Have, Richard Warner, Robert Sloan

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A cost/benefit approach to privacy confronts two tradeoff issues. One is making appropriate tradeoffs between privacy and many goals served by the collection, distribution, and use of information. The other is making tradeoffs between investments in preventing unauthorized access to information and the variety of other goals that also make money, time, and effort demands. Much has been written about the first tradeoff. We focus on the second. The issue is critical. Data breaches occur at the rate of over three a day, and the aggregate social cost is extremely high. The puzzle is that security experts have long explained …

Electricity Markets And The Social Project Of Decarbonization, Shelley Welton

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Decarbonization is the process of converting our economy from one that runs predominantly on energy derived from fossil fuels to one that runs almost exclusively on clean, carbon-free energy. If pursued on the scale that experts believe necessary to prevent dangerous climate change, the infrastructure changes required to decarbonize the United States will have significant social and cultural implications. States aggressively pursuing decarbonization have adopted policies reflecting their understanding that decarbonization is a social project implicating numerous value choices. Various state decarbonization policies combine the aim of decarbonization with job promotion, economic development, income redistribution, urban revitalization, open-space preservation, and …

The Logic And Limits Of Event Studies In Securities Fraud Litigation, Jill E. Fisch, Jonah B. Gelbach, Jonathan Klick

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Event studies have become increasingly important in securities fraud litigation after the Supreme Court’s decision in Halliburton II. Litigants have used event study methodology, which empirically analyzes the relationship between the disclosure of corporate information and the issuer’s stock price, to provide evidence in the evaluation of key elements of federal securities fraud, including materiality, reliance, causation, and damages. As the use of event studies grows and they increasingly serve a gatekeeping function in determining whether litigation will proceed beyond a preliminary stage, it will be critical for courts to use them correctly.

This Article explores an array of …

Ispy: Threats To Individual And Institutional Privacy In The Digital World, Lori Andrews

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What type of information is collected, who is viewing it, and what law librarians can do to protect their patrons and institutions.

Error Costs, Legal Standards Of Proof And Statistical Significance, Michelle Burtis, Jonah B. Gelbach, Bruce H. Kobayashi

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The relationship between legal standards of proof and thresholds of statistical significance is a well-known and studied phenomena in the academic literature. Moreover, the distinction between the two has been recognized in law. For example, in Matrix v. Siracusano, the Court unanimously rejected the petitioner’s argument that the issue of materiality in a securities class action can be defined by the presence or absence of a statistically significant effect. However, in other contexts, thresholds based on fixed significance levels imported from academic settings continue to be used as a legal standard of proof. Our positive analysis demonstrates how a …