Law Commons^™

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, …

Automated Government For Vulnerable Citizens: Intermediating Rights, Sofia Ranchordás, Luisa Scarcella

William & Mary Bill of Rights Journal

Filing tax returns or applying for unemployment benefits are some of the most common government transactions. Yet interacting with tax and social security authorities is for many a source of government anxiety. Bureaucracy, regulatory delays, and the complexity of the administrative legal system have been regarded for decades as the key reasons for this problem. Digital government promised a solution in the shape of simplified forms, electronic filing, and better communication with citizens. In the United States, privately developed software systems such as TurboTax and MiDAS emerged as intermediaries between citizens and digital government, selling convenience and efficiency. These systems …

Assessing Automated Administration, Cary Coglianese, Alicia Lai

All Faculty Scholarship

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

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, …

Professional Speech At Scale, Cassandra Burke Robertson, Sharona Hoffman

Faculty Publications

Regulatory actions affecting professional speech are facing new challenges from all sides. On one side, the Supreme Court has grown increasingly protective of professionals’ free speech rights, and it has subjected regulations affecting that speech to heightened levels of scrutiny that call into question traditional regulatory practices in both law and medicine. On the other side, technological developments, including the growth of massive digital platforms and the introduction of artificial intelligence programs, have created brand new problems of regulatory scale. Professional speech is now able to reach a wide audience faster than ever before, creating risks that misinformation will cause …

Algorithm Vs. Algorithm, Cary Coglianese, Alicia Lai

All Faculty Scholarship

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

All Faculty Scholarship

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

All Faculty Scholarship

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. …

Hukum Persaingan 4.0: Issue Bigdata, Artificial Intelligence Dan Blockchain Dalam Konteks Hukum Persaingan Usaha Di Era Industri Ekonomi Digital, Aldo Suhartono Putra

"Dharmasisya” Jurnal Program Magister Hukum FHUI

The purpose of the Antitrust Law which is regulated under Indonesian Law Number 5 of 1999 concerning Prohibition of Monopoly Practices and Unfair Business Competition, is to create market efficiency by preventing monopoly, both productive efficiency and allocative efficiency. Methods that can be used to identify anti-competitive practices are per-se-illegal and rule-of-reason. The era of the digital economy industry has changed the landscape of world economic activity, the presence of Arficial Intelligence, Big Data, and Blockchain, in certain dimensions can help us reach markets more efficiently, but in other dimensions, the presence of this technology makes the elements and characteristics …

Peran Hukum Dalam Implementasi Universal Basic Income Sebagai Alternatif Kebijakan Fiskal Masa Depan Di Indonesia, Muhammad Arsjad Yusuf

"Dharmasisya” Jurnal Program Magister Hukum FHUI

In a decade or two, automation and artificial intelligence are prognosticated to supersede humans in fields of work and dramatically decrease labor market. Such circumstances will drastically escalate unemployment and fertilize poverty and economic inequality which are antecedently acute matters in Indonesia. These economic state of affairs are susceptible to generate social upheavals which are prerequisite for turmoil and national disintegration. This article will explain automation and artificial intelligence and their adverse effects on Indonesian economy. Subsequently, as a proposed solution to deal with adverse effects of automation and artificial intelligence, Universal Basic Income will be elaborated in its benefits …

Ai In Adjudication And Administration, Cary Coglianese, Lavi M. Ben Dor

All Faculty Scholarship

The use of artificial intelligence has expanded rapidly in recent years across many aspects of the economy. For federal, state, and local governments in the United States, interest in artificial intelligence has manifested in the use of a series of digital tools, including the occasional deployment of machine learning, to aid in the performance of a variety of governmental functions. In this paper, we canvas the current uses of such digital tools and machine-learning technologies by the judiciary and administrative agencies in the United States. Although we have yet to see fully automated decision-making find its way into either adjudication …

Administrative Law In The Automated State, Cary Coglianese

All Faculty Scholarship

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

All Faculty Scholarship

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

All Faculty Scholarship

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 …

Data-Informed Duties In Ai Development, Frank A. Pasquale

Faculty Scholarship

Law should help direct—and not merely constrain—the development of artificial intelligence (AI). One path to influence is the development of standards of care both supplemented and informed by rigorous regulatory guidance. Such standards are particularly important given the potential for inaccurate and inappropriate data to contaminate machine learning. Firms relying on faulty data can be required to compensate those harmed by that data use—and should be subject to punitive damages when such use is repeated or willful. Regulatory standards for data collection, analysis, use, and stewardship can inform and complement generalist judges. Such regulation will not only provide guidance to …

Transparency And Algorithmic Governance, Cary Coglianese, David Lehr

All Faculty Scholarship

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 …

Information And The Regulatory Landscape: A Growing Need To Reconsider Existing Legal Frameworks, Anjanette H. Raymond

Washington and Lee Journal of Civil Rights and Social Justice

No abstract provided.

Regulating By Robot: Administrative Decision Making In The Machine-Learning Era, Cary Coglianese, David Lehr

All Faculty Scholarship

Machine-learning algorithms are transforming large segments of the economy, underlying everything from product marketing by online retailers to personalized search engines, and from advanced medical imaging to the software in self-driving cars. As machine learning’s use has expanded across all facets of society, anxiety has emerged about the intrusion of algorithmic machines into facets of life previously dependent on human judgment. Alarm bells sounding over the diffusion of artificial intelligence throughout the private sector only portend greater anxiety about digital robots replacing humans in the governmental sphere. A few administrative agencies have already begun to adopt this technology, while others …

Optimizing Government For An Optimizing Economy, Cary Coglianese

All Faculty Scholarship

Much entrepreneurial growth in the United States today emanates from technological advances that optimize through contextualization. Innovations as varied as Airbnb and Uber, fintech firms and precision medicine, are transforming major sectors in the economy by customizing goods and services as well as refining matches between available resources and interested buyers. The technological advances that make up the optimizing economy create new challenges for government oversight of the economy. Traditionally, government has overseen economic activity through general regulations that aim to treat all individuals equally; however, in the optimizing economy, business is moving in the direction of greater individualization, not …

The Scored Society: Due Process For Automated Predictions, Danielle Keats Citron, Frank A. Pasquale

Danielle Keats Citron

Big Data is increasingly mined to rank and rate individuals. Predictive algorithms assess whether we are good credit risks, desirable employees, reliable tenants, valuable customers—or deadbeats, shirkers, menaces, and “wastes of time.” Crucial opportunities are on the line, including the ability to obtain loans, work, housing, and insurance. Though automated scoring is pervasive and consequential, it is also opaque and lacking oversight. In one area where regulation does prevail—credit—the law focuses on credit history, not the derivation of scores from data.

Procedural regularity is essential for those stigmatized by “artificially intelligent” scoring systems. The American due process tradition should inform …

The Scored Society: Due Process For Automated Predictions, Danielle Keats Citron, Frank A. Pasquale

Frank A. Pasquale

Big Data is increasingly mined to rank and rate individuals. Predictive algorithms assess whether we are good credit risks, desirable employees, reliable tenants, valuable customers—or deadbeats, shirkers, menaces, and “wastes of time.” Crucial opportunities are on the line, including the ability to obtain loans, work, housing, and insurance. Though automated scoring is pervasive and consequential, it is also opaque and lacking oversight. In one area where regulation does prevail—credit—the law focuses on credit history, not the derivation of scores from data. Procedural regularity is essential for those stigmatized by “artificially intelligent” scoring systems. The American due process tradition should inform …

The Scored Society: Due Process For Automated Predictions, Danielle Keats Citron, Frank A. Pasquale

Faculty Scholarship

Big Data is increasingly mined to rank and rate individuals. Predictive algorithms assess whether we are good credit risks, desirable employees, reliable tenants, valuable customers—or deadbeats, shirkers, menaces, and “wastes of time.” Crucial opportunities are on the line, including the ability to obtain loans, work, housing, and insurance. Though automated scoring is pervasive and consequential, it is also opaque and lacking oversight. In one area where regulation does prevail—credit—the law focuses on credit history, not the derivation of scores from data.

Procedural regularity is essential for those stigmatized by “artificially intelligent” scoring systems. The American due process tradition should inform …