Smiles in Profiles: Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces

Susan Athey, Dean Karlan, Emil Palikot and Yuan Yuan
Additional contact information
Emil Palikot: Stanford U
Yuan Yuan: Carnegie Mellon U

Research Papers from Stanford University, Graduate School of Business

Abstract: 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, we find that choices made by borrowers creating online profiles impact both of these objectives. We further support this conclusion with a web-based randomized survey experiment. In the experiment, we create profile images using Generative Adversarial Networks that differ in a specific feature and estimate its impact on lender demand. We then counterfactually evaluate alternative platform policies and identify particular approaches to influencing the changeable profile photo features that can ameliorate the fairness-efficiency tension.

JEL-codes: D0 D41 J0 O1 (search for similar items in EconPapers)
Date: 2022-11
New Economics Papers: this item is included in nep-big, nep-cmp and nep-pay
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Citations: View citations in EconPapers (3)

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Working Paper: Smiles in Profiles: Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:ecl:stabus:4071

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