Managing fairness risk of AI in consumer finance

David M. Skanderson and Adam H. Gailey

Chapter Chapter 21 in Research Handbook on the Law of Artificial Intelligence, 2025, pp 443-466 from Edward Elgar Publishing

Abstract: The risk of unknowing and unintentional discrimination is an increasing concern with the application of complex machine learning and artificial intelligence models to make decisions about individuals in such areas as credit, housing, employment, incarceration, fraud prevention and identity verification, among others. Models that inadvertently embed bias based on legally protected personal characteristics (e.g., race or sex) can have serious adverse consequences for individuals and expose the developers and users of such models to legal risk. The objective of this chapter is to provide legal professionals and others with an introduction to key concepts and considerations in the evaluation of predictive models for discriminatory effects, and common approaches for analyzing and remediating the risk of illegal discrimination. We discuss how inadvertent discriminatory effects can arise in AI models, approaches to identifying and measuring fairness risk, the importance of rigorously establishing a model’s business justification, challenges with searching for alternative models with improved fairness properties, and the elements of an approach to proactively managing the fairness risk of AI models.

Keywords: Consumer finance; Discrimination; Fairness risk; Predictive mode; Artificial intelligence (search for similar items in EconPapers)
Date: 2025
ISBN: 9781035316489
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