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Smiles in Profiles: Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces (WP-22-53)

Susan Athey, Dean Karlan, Emil Palikot, and Yuan Yuan

Online platforms often face challenges being both fair (i.e., non-discriminatory) and efficient (i.e., maximizing revenue). Using computer vision algorithms and observational data from a microlending marketplace, the researchers find that choices made by borrowers creating online profiles impact both of these objectives. They further support this conclusion with a web-based randomized survey experiment. In the experiment, they create profile images using Generative Adversarial Networks that differ in a specific feature and estimate its impact on lender demand. The authors then counterfactually evaluate alternative platform policies and identify particular approaches to influencing the changeable profile photo features that can ameliorate the fairness-efficiency tension.

Susan Athey, The Economics of Technology Professor, Stanford University

Dean Karlan, Professor of Economics and Finance, Frederic Esser Nemmers Chair, and IPR Associate, Northwestern University

Emil Palikot, Postdoctoral Researcher, Graduate School of Business, Stanford University

Yuan Yuan, Tepper School of Business, Carnegie Mellon University

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