By Joao Guerreiro
Joao Guerreiro
The short stories in Isaac Asimov’s “I, Robot” discussed some of the dilemmas associated with artificial intelligence (AI). New developments in AI technology have brought these concerns from the pages of science-fiction stories to the forefront of policy discussions. In May 2023, a global AI consortium declared AI risks a priority, similar to pandemics and nuclear war. The G7 started the Hiroshima AI Process for global AI regulation. Both the European Union and the U.S. are discussing regulatory frameworks. Ideas include mandatory testing, holding developers accountable, and classifying AI into risk tiers. The critical issue is the uncertainty surrounding AI’s societal costs and benefits.
How should AI be regulated in the presence of uncertainty regarding the AI’s potential adverse external effects? Professor Joao Guerreiro, with Sergio Rebelo (Kellogg School of Management) and Pedro Teles (Banco de Portugal and Catolica-Lisbon School of Business and Economics), tackles this question in a recent paper: “Regulating Artificial Intelligence.” The paper evaluates regulatory approaches using a normative analysis. The authors explore two settings. In the first scenario, uncertainty is only resolved after the release of the AI. In the second scenario, it is possible to beta-test the algorithm to assess external effects.
In the absence of beta testing, there’s a mismatch between the optimal AI novelty level for society and what naturally arises in an unregulated market. The paper argues that the social optimum, or the ideal balance of AI novelty and safety, is generally more conservative than what the market would select without regulation. With beta testing, developers can learn the external effects of the AI by testing before release. This approach helps resolve uncertainties regarding potential negative externalities. Still, the social optimum requires a higher degree of conservativism both in testing and the algorithm’s release.
The authors evaluate three regulatory frameworks. First, they show that subjecting algorithms to regulatory approval is insufficient to implement the social optimum– since developers still have the incentive to go for too-risky algorithms. Second, simply holding developers liable for the external effects of the algorithms is also insufficient to implement the social optimum in the presence of limited liability. Finally, they show that mandating beta testing to assess the externalities and holding developers liable for the adverse effects of the algorithm can achieve the social optimum, even in the presence of limited liability.
Overall, the paper’s findings highlight the complexity of AI regulation and the need for nuanced approaches that balance innovation and safety. By considering various scenarios and regulatory frameworks, it provides valuable insights for policymakers and stakeholders in the AI field.
UCLA Professor Bernardo Silveira guest editor of the Journal of Economic Behavior & Organization
/in News /by Jenail MobarakaThe Journal of Economic Behavior & Organization has invited UCLA Professor Bernardo Silveira to be a guest editor for the issue on ‘Conflict, Distribution, and Efficiency in Bargaining’. Details about the issue can be found here.
Former UCLA Graduate Student Fernanda Rojas-Ampuero Wins the 2024 Dorothy Thomas Award
/in News /by Jenail MobarakaFormer UCLA Graduate Student Fernanda Rojas-Ampuero, now a Professor in the Department of Economics at the University of Wisconsin, won the Population Association of America’s highly competitive 2024 Dorothy Thomas Award for best graduate student paper. Her paper, entitled “Sent Away: The Long-Term Effects of Slum Clearance on Children and Families,” documents how Chile’s mandated slum-clearance programs between 1979-1985 had large, negative long-run effects on children and parents. Displaced children earned 14% less as adults, achieved 0.64 fewer years of education, and were more likely to work in informal jobs. Displaced parents had higher mortality rates and died at younger ages. While at UCLA, Fernanda was a recipient of CCPR’s Treiman award, and received her Ph.D. in economics in 2022. Her dissertation was advised by Professors Dora Costa (chair), Adriana Lleras-Muney, and Michela Giorcelli.
UCLA graduate student Nate Barlow wins the Economic History Early Stage Dissertation Grant
/in News /by Jenail MobarakaNate Barlow, a graduate student in the UCLA economics department has won the Economic History Early Stage Dissertation Grant for his project on reparations paid to Japanese internees. The grant is awarded to the most promising dissertations in economic history.
UCLA Professors Board and Meyer-ter-Vehn receive AEJ Best Paper Award
/in News /by Jenail MobarakaUCLA Professors Simon Board and Moritz Meyer-ter-Vehn received the American Economic Journal (AEJ) Best Paper Award for their paper ‘A Reputational Theory of Firm Dynamics’ published in the American Economic Journal: Microeconomics in 2022. The annual AEJ Best Paper Award is given to the best paper published in each of the American Economic Journals: Applied Economics, Economic Policy, Macroeconomics, and Microeconomics over the last three years.
The announcement can be found here.
The paper here.
UCLA Professor Martha Bailey Elected a Fellow of the Cliometric Society
/in News /by Jenail MobarakaProfessor Martha Bailey was elected a fellow of the cliometric society. Fellows must have published contributions to economic history that are markedly original and have significantly advanced the frontiers of knowledge.
A complete list of Fellows can be found here.
UCLA Professor Martha Bailey Receives Honorary Doctorate from Lund University
/in News /by Jenail MobarakaPaper by UCLA Professor Pierre-Olivier Weill was featured in NBER Digest
/in News /by Jenail MobarakaA paper by UCLA Professor Pierre-Olivier Weill studying bond trading was featured in the February issue of NBER Digest. The paper finds that, in addition to the characteristics of a bond trade request, the characteristics of the bond trader are important in determining the time to consummating a trade and the failure rate.
The February issue of NBER Digest can be found here.
The paper can be found here.
Should we regulate artificial intelligence?
/in Research Spotlight /by Jenail MobarakaBy Joao Guerreiro
Joao Guerreiro
The short stories in Isaac Asimov’s “I, Robot” discussed some of the dilemmas associated with artificial intelligence (AI). New developments in AI technology have brought these concerns from the pages of science-fiction stories to the forefront of policy discussions. In May 2023, a global AI consortium declared AI risks a priority, similar to pandemics and nuclear war. The G7 started the Hiroshima AI Process for global AI regulation. Both the European Union and the U.S. are discussing regulatory frameworks. Ideas include mandatory testing, holding developers accountable, and classifying AI into risk tiers. The critical issue is the uncertainty surrounding AI’s societal costs and benefits.
How should AI be regulated in the presence of uncertainty regarding the AI’s potential adverse external effects? Professor Joao Guerreiro, with Sergio Rebelo (Kellogg School of Management) and Pedro Teles (Banco de Portugal and Catolica-Lisbon School of Business and Economics), tackles this question in a recent paper: “Regulating Artificial Intelligence.” The paper evaluates regulatory approaches using a normative analysis. The authors explore two settings. In the first scenario, uncertainty is only resolved after the release of the AI. In the second scenario, it is possible to beta-test the algorithm to assess external effects.
In the absence of beta testing, there’s a mismatch between the optimal AI novelty level for society and what naturally arises in an unregulated market. The paper argues that the social optimum, or the ideal balance of AI novelty and safety, is generally more conservative than what the market would select without regulation. With beta testing, developers can learn the external effects of the AI by testing before release. This approach helps resolve uncertainties regarding potential negative externalities. Still, the social optimum requires a higher degree of conservativism both in testing and the algorithm’s release.
The authors evaluate three regulatory frameworks. First, they show that subjecting algorithms to regulatory approval is insufficient to implement the social optimum– since developers still have the incentive to go for too-risky algorithms. Second, simply holding developers liable for the external effects of the algorithms is also insufficient to implement the social optimum in the presence of limited liability. Finally, they show that mandating beta testing to assess the externalities and holding developers liable for the adverse effects of the algorithm can achieve the social optimum, even in the presence of limited liability.
Overall, the paper’s findings highlight the complexity of AI regulation and the need for nuanced approaches that balance innovation and safety. By considering various scenarios and regulatory frameworks, it provides valuable insights for policymakers and stakeholders in the AI field.
David Henning was awarded this year’s Summit Fellowship in Applied Economics
/in News /by Jenail MobarakaUCLA Professor Andres Santos named coeditor of the American Economic Review
/in News /by Jenail MobarakaUCLA Professor Andres Santos has been appointed as coeditor of the American Economic Review, the leading journal in economics.