  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.
