Analyzing Bias in Sensitive Personal Information Used to Train Financial Models
This addresses bias and privacy risks in financial services, though it appears incremental by building on existing anonymization and fairness techniques.
The paper tackles bias and privacy issues in financial data by developing a trusted model-lifecycle management platform that reproduces datasets using deep learning to retain statistical features while protecting privacy, enabling secure sharing.
Bias in data can have unintended consequences that propagate to the design, development, and deployment of machine learning models. In the financial services sector, this can result in discrimination from certain financial instruments and services. At the same time, data privacy is of paramount importance, and recent data breaches have seen reputational damage for large institutions. Presented in this paper is a trusted model-lifecycle management platform that attempts to ensure consumer data protection, anonymization, and fairness. Specifically, we examine how datasets can be reproduced using deep learning techniques to effectively retain important statistical features in datasets whilst simultaneously protecting data privacy and enabling safe and secure sharing of sensitive personal information beyond the current state-of-practice.