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

Susan Athey, Dean Karlan, Emil Palikot and Yuan Yuan

No 30633, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Online platforms often face the challenge of being both fair (i.e., non-discriminatory) and efficient (i.e., maximizing revenue). Using computer vision algorithms and observational data from a micro-lending marketplace, we find that the choices that online borrowers make when creating online profiles impact both of these objectives. We further support this finding 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 the impact of the feature on lender demand. We then evaluate counterfactual platform policies based on the changeable profile features, and identify approaches that can ameliorate the fairness-efficiency tension.

JEL-codes: D0 D40 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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Working Paper: Smiles in Profiles: Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces (2022)
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