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Articles 1 - 12 of 12
Full-Text Articles in Public Administration
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 …
Contracting For Algorithmic Accountability, Cary Coglianese, Erik Lampmann
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 …
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 …
Management-Based Regulation, Cary Coglianese, Shana M. Starobin
Management-Based Regulation, Cary Coglianese, Shana M. Starobin
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Environmental regulators have embraced management-based regulation as a flexible instrument for addressing a range of important problems often poorly addressed by other types of regulations. Under management-based regulation, regulated firms must engage in management-related activities oriented toward addressing targeted problems—such as planning and analysis to mitigate risk and the implementation of internal management systems geared towards continuous improvement. In contrast with more restrictive forms of regulation which can impose one-size-fits-all solutions, management-based regulation offers firms greater operational choice about how to solve regulatory problems, leveraging firms’ internal informational advantage to innovate and search for alternative measures to achieve the intended …
Illuminating Regulatory Guidance, Cary Coglianese
Illuminating Regulatory Guidance, Cary Coglianese
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Administrative agencies issue many guidance documents each year in an effort to provide clarity and direction to the public about important programs, policies, and rules. But these guidance documents are only helpful to the public if they can be readily found by those who they will benefit. Unfortunately, too many agency guidance documents are inaccessible, reaching the point where some observers even worry that guidance has become a form of regulatory “dark matter.” This article identifies a series of measures for agencies to take to bring their guidance documents better into the light. It begins by explaining why, unlike the …
Getting The Blend Right: Public-Private Partnerships In Risk Management, Cary Coglianese
Getting The Blend Right: Public-Private Partnerships In Risk Management, Cary Coglianese
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The question of whether there is too much or too little regulation in the United States has driven much political debate for decades. The more important question, though, is not about getting the right amount of regulation but it is about finding the best ways for the public and private sectors to interact. When it comes to managing risk in society, this latter question is necessarily one of choosing between different kinds of structures—or partnerships—between public and private institutions. Sometimes these partnerships are adversarial, as they can be with government regulation. Other times they are seemingly invisible, such as when …
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 …
Optimizing Government For An Optimizing Economy, Cary Coglianese
Optimizing Government For An Optimizing Economy, Cary Coglianese
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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 …
Thinking Ahead, Looking Back: Assessing The Value Of Regulatory Impact Analysis And Procedures For Its Use, Cary Coglianese
Thinking Ahead, Looking Back: Assessing The Value Of Regulatory Impact Analysis And Procedures For Its Use, Cary Coglianese
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Analysis is a tool for making important legislative and regulatory decisions but it is also a way of looking back to see whether decisions made in the past have been good ones. How well have legal rules actually worked in practice? Answering this question is crucial, not only for improving regulation and legislation in the future, but also for improving forward-looking regulatory impact analysis (RIA). This article was originally presented as the keynote address at the 22nd Anniversary International Conference of the Korea Legislation Research Institute in August 2012. It highlights what social scientists have told us generally about the …