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Cited by 24Ethics and Social Impacts of AIFinancial Distress and Bankruptcy PredictionFinTech, Crowdfunding, Digital Finance

Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulation

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Kumar, I. Elizabeth, Hines, Keegan E., Dickerson, John P.·2022·Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society
DOI 10.1145/3514094.3534154
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Abstract

Credit is an essential component of financial wellbeing in America, and unequal access to it is a large factor in the economic disparities between demographic groups that exist today. Today, machine learning algorithms, sometimes trained on alternative data, are increasingly being used to determine access to credit, yet research has shown that machine learning can encode many different versions of "unfairness," thus raising the concern that banks and other financial institutions could---potentially unwittingly---engage in illegal discrimination through the use of this technology. In the US, there are laws in place to make sure discrimination does not happen in lending and agencies charged with enforcing them. However, conversations around fair credit models in computer science and in policy are often misaligned: fair machine learning research often lacks legal and practical considerations specific to existing fair lending policy, and regulators have yet to issue new guidance on how, if at all, credit risk models should be utilizing practices and techniques from the research community. This paper aims to better align these sides of the conversation. We describe the current state of credit discrimination regulation in the United States, contextualize results from fair ML research to identify the specific fairness concerns raised by the use of machine learning in lending, and discuss regulatory opportunities to address these concerns.

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References 35 · Cited by 24

Research works printed but not linked: 24

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Authors
Kumar, I. E., Hines, K. E., & Dickerson, J. P.
Affiliations
  • Brown University (US)
  • Arthur AI, New York City, NY, USA
ORCID
Venue
Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society
Publisher
ACM
Published
2022-07-26
Pages
357-368
ISSN
3065-8365 (ISSN-L)
DOI
10.1145/3514094.3534154
OpenAlex
W4288096742
Cited by
24
References
35
Citations by year
2026: 7 · 2025: 6 · 2024: 4 · 2023: 7
FWCI
4.22
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • Proceedings of the AAAI/ACM Conference on AI Ethics and Society (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
  • Social Sciences › Business, Management and Accounting › Management Information Systems › FinTech, Crowdfunding, Digital Finance
Keywords
credit discrimination, algorithmic fairness, access to credit, algorithmic discrimination, credit risk modeling, economic inequality, regulatory guidelines, United States

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