Promoting Fairness and Exploring Algorithmic Discrimination in Financial Decision Making through Explainable Artificial Intelligence

Research output: Book/ReportCommissioned report

Abstract

In this white paper a comprehensive toolbox is developed, grounded in an ethical "rights to explanation" framework, deploying state-of-the-art machine learning/artificial intelligence models, through the lens of explainability. Harnessing these explainable artificial intelligence algorithms within the toolbox, we propose implementing an ensemble of model-agnostic techniques, to improve fairness in financial decision making, with a particular focus on US home mortgage loan applications with a granular public dataset. We also highlight variability in these techniques, imposing various pragmatic scenarios that explore real-world decision making, alongside equality of opportunity and equality of outcome conditions. We highlight potential pitfalls, nuances, and possible innovations in applying these techniques, while providing the ability to simultaneously assess the impact of any specific variable in decision making, and a model’s performance in such decision making, with established machine learning criteria. Furthermore, we showcase the trade-off between fairness and model performance optimization with a protected characteristic (age) that might form the basis of plausibly discriminatory practices in such a context. Our study aims to be in the spirit of Agarwal, Muckley, & Neelakantan (2023), Kelley, Ovchinnikov, Hardoon, & Heinrich, (2022), Kozodoi, Jacob, & Lessmann (2022), and Kim & Routledge (2022), among others. We lastly identify areas for future research.
Original languageEnglish
Place of PublicationGlasgow
PublisherUniversity of Strathclyde
Commissioning bodyFinTech Scotland
Number of pages27
Publication statusPublished - 30 Jun 2024

Publication series

NameFinancial Regulation Innovation Lab White Paper Series
PublisherUniversity of Strathclyde
ISSN (Electronic)3033-4136

Keywords

  • algorithmic discrimination
  • fairness
  • financial decision making

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