Algorithmic Harm in Consumer Markets

64 Pages Posted: 10 Jan 2023 Last revised: 15 Mar 2023

See all articles by Oren Bar-Gill

Oren Bar-Gill

Harvard Law School

Cass R. Sunstein

Harvard Law School; Harvard University - Harvard Kennedy School (HKS)

Inbal Talgam-Cohen

Technion-Israel Institute of Technology

Date Written: January 10, 2023

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 individualization. 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 disproportionately affect members of identifiable groups, including women and people of color. Since algorithms 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 challenging; 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 exploitation of an absence of information or of behavioral biases.

Keywords: Algorithms, Price Discrimination, Targeting, Cognitive Biases, Misperceptions, Imperfect Information, Imperfect Rationality, Consumer Protection, Antidiscrimination Law, Explainability, Interpretability

JEL Classification: D11, D18, D91, K23, L11

Suggested Citation

Bar-Gill, Oren and Sunstein, Cass R. and Talgam-Cohen, Inbal, Algorithmic Harm in Consumer Markets (January 10, 2023). Harvard Public Law Working Paper No. 23-05, Available at SSRN: https://ssrn.com/abstract=4321763 or http://dx.doi.org/10.2139/ssrn.4321763

Oren Bar-Gill (Contact Author)

Harvard Law School ( email )

1575 Massachusetts
Hauser 406
Cambridge, MA 02138
United States

Cass R. Sunstein

Harvard Law School ( email )

1575 Massachusetts Ave
Areeda Hall 225
Cambridge, MA 02138
United States
617-496-2291 (Phone)

Harvard University - Harvard Kennedy School (HKS) ( email )

79 John F. Kennedy Street
Cambridge, MA 02138
United States

Inbal Talgam-Cohen

Technion-Israel Institute of Technology ( email )

Technion City
Haifa 32000, Haifa 32000
Israel

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