We obtain point identification and propose a debiased machine learning estimator, derive its asymptotic distribution, and show how this can be used to carry out inference for the FA-frontier.
Archives
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Caputo’s work focuses on frontier AI regulation, legal alignment, open source AI, and how to build governance institutions for the AI era.
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Levon Barseghyan’s work, both theoretical and empirical, is aimed at uncovering mechanisms that shape economic behavior and how such behavior is affected by market structure, economic policies and political institutions.
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Limited consideration is indispensable for credible inference and welfare analysis of choice under risk in field environments.
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Gillian K. Hadfield’s research focuses on innovative design for legal, regulatory, and technical systems for AI, computational models of human normative systems, and building AI systems and agents that understand and respond to human values and norms.
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While LLMs should be kept well clear of formal democratic decision-making processes, we think they can instead strengthen the informal public sphere—the arena that mediates between democratic governments and the polities that they serve, in which political communities seek information, form civic publics, and hold their leaders to account.
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The 1995 Trade Related Intellectual Property Rights Agreement (TRIPS) increased the likelihood of strong patent protection in India, and drugs under the TRIPS regime face significantly less competition.
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The “Fourth Settlement” framework of administrative law escapes the capability-accountability trap by preserving capability while restoring comprehensible oversight of administration.
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How can legal rules, principles, and methods can be leveraged to address problems of alignment and inform the design of AI systems that operate safely and ethically?
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For most parameter values, the test is optimal subject to some arguably plausible desiderata; also, we illustrate why some such desiderata are necessary.