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Full-Text Articles in Law
Assessing Automated Administration, Cary Coglianese, Alicia Lai
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. …
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 …
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 …
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 …
Regulating By Robot: Administrative Decision Making In The Machine-Learning Era, Cary Coglianese, David Lehr
Regulating By Robot: Administrative Decision Making In The Machine-Learning Era, Cary Coglianese, David Lehr
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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
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 …