An Introduction to Artificial Intelligence and Solutions to the Problems of Algorithmic Discrimination
It tackles fairness issues for protected classes in regulated domains like credit, but is incremental as it builds on existing legal frameworks.
The paper addresses algorithmic discrimination in AI/ML, particularly in consumer credit, by proposing a methodology to evaluate fairness and minimize bias in compliance with anti-discrimination laws, arguing that risks can be mitigated with proper understanding.
There is substantial evidence that Artificial Intelligence (AI) and Machine Learning (ML) algorithms can generate bias against minorities, women, and other protected classes. Federal and state laws have been enacted to protect consumers from discrimination in credit, housing, and employment, where regulators and agencies are tasked with enforcing these laws. Additionally, there are laws in place to ensure that consumers understand why they are denied access to services and products, such as consumer loans. In this article, we provide an overview of the potential benefits and risks associated with the use of algorithms and data, and focus specifically on fairness. While our observations generalize to many contexts, we focus on the fairness concerns raised in consumer credit and the legal requirements of the Equal Credit and Opportunity Act. We propose a methodology for evaluating algorithmic fairness and minimizing algorithmic bias that aligns with the provisions of federal and state anti-discrimination statutes that outlaw overt, disparate treatment, and, specifically, disparate impact discrimination. We argue that while the use of AI and ML algorithms heighten potential discrimination risks, these risks can be evaluated and mitigated, but doing so requires a deep understanding of these algorithms and the contexts and domains in which they are being used.