Algorithmic Fairness

Sanjiv Das, Richard Stanton and Nancy Wallace ()
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Sanjiv Das: Leavey School of Business, Santa Clara University, Santa Clara, California, USA
Richard Stanton: Haas School of Business, University of California, Berkeley, California, USA
Nancy Wallace: Haas School of Business, University of California, Berkeley, California, USA

Annual Review of Financial Economics, 2023, vol. 15, issue 1, 565-593

Abstract: This article reviews the recent literature on algorithmic fairness, with a particular emphasis on credit scoring. We discuss human versus machine bias, bias measurement, group versus individual fairness, and a collection of fairness metrics. We then apply these metrics to the US mortgage market, analyzing Home Mortgage Disclosure Act data on mortgage applications between 2009 and 2015. We find evidence of group imbalance in the dataset for both gender and (especially) minority status, which can lead to poorer estimation/prediction for female/minority applicants. Loan applicants are handled mostly fairly across both groups and individuals, though we find that some local male (nonminority) neighbors of otherwise similar rejected female (minority) applicants were granted loans, something that warrants further study. Finally, modern machine learning techniques substantially outperform logistic regression (the industry standard), though at the cost of being substantially harder to explain to denied applicants, regulators, or the courts.

Keywords: algorithms; machine learning; bias; fairness metrics; credit scoring (search for similar items in EconPapers)
JEL-codes: C55 G21 G23 G28 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1146/annurev-financial-110921-125930

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