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The success of deep learning in recent years have led to a significantincrease in interest and prevalence for its adoption to tackle financialservices tasks. One particular question that often arises as a barrier toadopting deep learning for financial services is whether the developedfinancial deep learning models are fair in their predictions, particularly inlight of strong governance and regulatory compliance requirements in thefinancial services industry. A fundamental aspect of fairness that has not beenexplored in financial deep learning is the concept of trust, whose variationsmay point to an egocentric view of fairness and thus provide insights into thefairness of models. In this study we explore the feasibility and utility of amulti-scale trust quantification strategy to gain insights into the fairness ofa financial deep learning model, particularly under different scenarios atdifferent scales. More specifically, we conduct multi-scale trustquantification on a deep neural network for the purpose of credit card defaultprediction to study: 1) the overall trustworthiness of the model 2) the trustlevel under all possible prediction-truth relationships, 3) the trust levelacross the spectrum of possible predictions, 4) the trust level acrossdifferent demographic groups (e.g., age, gender, and education), and 5)distribution of overall trust for an individual prediction scenario. Theinsights for this proof-of-concept study demonstrate that such a multi-scaletrust quantification strategy may be helpful for data scientists and regulatorsin financial services as part of the verification and certification offinancial deep learning solutions to gain insights into fairness and trust ofthese solutions.

Insights into Fairness through Trust: Multi-scale Trust Quantification for Financial Deep Learning