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

Jain, Kushagra and Bowden, James and Cummins, Mark (2024) Promoting Fairness and Exploring Algorithmic Discrimination in Financial Decision Making through Explainable Artificial Intelligence. University of Strathclyde, Glasgow.

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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.

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