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Articles 1 - 15 of 15
Full-Text Articles in Physical Sciences and Mathematics
Regulating Machine Learning: The Challenge Of Heterogeneity, Cary Coglianese
Regulating Machine Learning: The Challenge Of Heterogeneity, Cary Coglianese
All Faculty Scholarship
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, …
Moving Toward Personalized Law, Cary Coglianese
Moving Toward Personalized Law, Cary Coglianese
All Faculty Scholarship
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
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
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 …
Regulating New Tech: Problems, Pathways, And People, Cary Coglianese
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. …
Administrative Law In The Automated State, Cary Coglianese
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 …
Deploying Machine Learning For A Sustainable Future, Cary Coglianese
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
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 …
Making Sustainability Disclosure Sustainable, Jill E. Fisch
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 …
Transparency And Algorithmic Governance, Cary Coglianese, David Lehr
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 …
A Truly “Top Task”: Rulemaking And Its Accessibility On Agency Websites, Cary Coglianese
A Truly “Top Task”: Rulemaking And Its Accessibility On Agency Websites, Cary Coglianese
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Government websites provide an important location for public access and participation in the governmental process. However, despite a growing body of research on agency websites, researchers have so far ignored agency websites as a method of public contact over rulemaking. In this article, I report results from two systematic surveys conducted on regulatory agencies’ websites which reveal how much more agencies could do to improve public access to rulemaking. Agencies commonly succumb to pressures to organize their websites around their “top tasks”—but, regrettably, they too often define these key tasks in terms of the volume of user demand for information …
Enhancing Public Access To Online Rulemaking Information, Cary Coglianese
Enhancing Public Access To Online Rulemaking Information, Cary Coglianese
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One of the most significant powers exercised by federal agencies is their power to make rules. Given the importance of agency rulemaking, the process by which agencies develop rules has long been subject to procedural requirements aiming to advance democratic values of openness and public participation. With the advent of the digital age, government agencies have engaged in increasing efforts to make rulemaking information available online as well as to elicit public participation via electronic means of communication. How successful are these efforts? How might they be improved? In this article, I investigate agencies’ efforts to make rulemaking information available …
Against 'Individual Risk': A Sympathetic Critique Of Risk Assessment, Matthew D. Adler
Against 'Individual Risk': A Sympathetic Critique Of Risk Assessment, Matthew D. Adler
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"Individual risk" currently plays a major role in risk assessment and in the regulatory practices of the health and safety agencies that employ risk assessment, such as EPA, FDA, OSHA, NRC, CPSC, and others. Risk assessors use the term "population risk" to mean the number of deaths caused by some hazard. By contrast, "individual risk" is the incremental probability of death that the hazard imposes on some particular person. Regulatory decision procedures keyed to individual risk are widespread. This is true both for the regulation of toxic chemicals (the heartland of risk assessment), and for other health hazards, such as …
Shifting Sands: The Limits Of Science In Setting Risk Standards, Cary Coglianese, Gary E. Marchant
Shifting Sands: The Limits Of Science In Setting Risk Standards, Cary Coglianese, Gary E. Marchant
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Regulators need to rely on science to understand problems and predict the consequences of regulatory actions, but over reliance on science can actually contribute to, or at least deflect attention from, incoherent policymaking. In this article, we explore the problems with using science to justify policy decisions by analyzing the Environmental Protection Agency's recently revised air quality standards for ground-level ozone and particulate matter, some of the most significant regulations ever issued. In revising these standards, EPA mistakenly invoked science as the exclusive basis for its decisions and deflected attention from a remarkable series of inconsistencies. For example, even though …
E-Rulemaking: Information Technology And The Regulatory Process: New Directions In Digital Government Research, Cary Coglianese
E-Rulemaking: Information Technology And The Regulatory Process: New Directions In Digital Government Research, Cary Coglianese
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Electronic rulemaking, or e-rulemaking, offers the potential to overcome some of the informational challenges associated with developing regulations. E-rulemaking refers to the use of digital technologies in the development and implementation of regulations. The use of these technologies may help streamline and improve regulatory management, such as by helping agency staff retrieve and analyze vast quantities of information from diverse sources. By taking better advantage of advances in digital technologies, agencies might also be able to increase the public's access to and involvement in rulemaking. Part I of this article details the rulemaking process, outlining the procedures agencies must currently …