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Full-Text Articles in Public Administration
Moving Toward Personalized Law, Cary Coglianese
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
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, …
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 …
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 …
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 …
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 …