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15 J. Legal Analysis 1 (2023)

handle is hein.journals/jlegan15 and id is 1 raw text is: Journal of Legal Analysis, 2023, 15, 1-47 https://doi.org/10.1093/jla/Iaad003 Advance access publication 21 August 2023 Article Algorithmic Harm in Consumer Markets Oren Bar-Gill,` Cass R. Sunstein* and Inbal Talgam-Cohent * Harvard Law School, Cambridge, MA, USA t Technion-Israel Institute of Technology, The Henry and Marilyn Taub Faculty of Computer Science, Haifa, Israel. Abstract Machine learning algorithms are increasingly able to predict what goods and services particular people will buy, and at what price. It is possible to imagine a situation in which relatively uniform, or coarsely set, prices and product characteristics are replaced by far more in the way of individu- alization. Companies might, for example, offer people shirts and shoes that are particularly suited to their situations, that fit with their particular tastes, and that have prices that fit their personal valuations. In many cases, the use of algorithms promises to increase efficiency and to promote social welfare; it might also promote fair distribution. But when consumers suffer from an absence of information or from behavioral biases, algorithms can cause serious harm. Companies might, for example, exploit such biases in order to lead people to purchase products that have little or no value for them or to pay too much for products that do have value for them. Algorithmic harm, understood as the exploitation of an absence of information or of behavioral biases, can dispropor- tionately affect members of identifiable groups, including women and people of color. Since algo- rithms exacerbate the harm caused to imperfectly informed and imperfectly rational consumers, their increasing use provides fresh support for existing efforts to reduce information and rationality deficits, especially through optimally designed disclosure mandates. In addition, there is a more particular need for algorithm-centered policy responses. Specifically, algorithmic transparency- transparency about the nature, uses, and consequences of algorithms-is both crucial and challen- ging; novel methods designed to open the algorithmic black box and interpret the algorithm's decision-making process should play a key role. In appropriate cases, regulators should also police the design and implementation of algorithms, with a particular emphasis on the exploitation of an absence of information or of behavioral biases. 1. Introduction Sellers and service providers are increasingly using machine learning algorithms. Many uses should greatly benefit consumers. Suppose that algorithms can predict what goods and services people will buy and at what price. If algorithms give people information about beneficial health care products that are ideally suited to their particular situations (say, diabetes or heart disease), consumers might gain For helpful comments and conversations, we thank Todd Baker, Omri Ben-Shahar, Ben Eidelson, Merritt Fox, Talia Gillis, Shafi Goldwasser, Zohar Goshen, Assaf Hamdani, Sharon Hannes, Howell Jackson, Louis Kaplow, Emiliano Katan, Avery Katz, Tamar Kricheli-Katz, Haggai Porat, Lucia Reisch, Ricky Revesz, Sarath Sanga, Alan Schwartz, Steve Shavell, Yonadav Shavit, Holger Spamann, Eric Talley, Rory Van Loo, Eyal Zamir, and workshop and conference participants at Columbia, Harvard, Tel-Aviv University, and at the 2022 Annual Meeting of the American Law and Economics Association. Ethan Judd, Rachel Neuburger, Davy Perlman, and Cecilia Wu provided excellent research assistance. See infra Parts II and IV See also Hogan (2018), describing how retailers use algorithms to tailor pricing and promotions, to customize search results, to personalize content, and more. Popular culture offers some complicated tales of personaliza- tion and individuation, with particular reference to algorithmic harm, such as the films Her (Annapurna Pictures 2013) and I'm You're Man (Letterbox Filmproduktion 2021). © The Author(s) 2023. Published by Oxford University Press on behalf of The John M. Olin Center for Law, Economics and Business at Harvard Law School. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

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