We discuss what United States anti-discrimination law is and isn’t, how it navigates tradeoffs, its limits, and how it applies to machine learning.

In this chapter, we hope to give you an appreciation of what United States anti-discrimination law is and isn’t. We’ll use the U.S. legal experience as a case study of how to regulate discrimination. Other countries take different approaches. We don’t aim to describe U.S. law comprehensively but rather give a stylized description of the key concepts.
We’ll start with a history of how the major civil rights statutes came to be, and draw lessons from this history that continue to be relevant today. Law represents one attempt to operationalize moral notions. It is an important and illustrative one. We will learn from the way in which the law navigates many tricky tradeoffs. But we will also study ist limitations and explain why we think algorithmic fairness shouldn’t stop at legal compliance.
The final section addresses the specifics of regulating machine learning. Although U.S. antidiscrimination law predates the widespread use of machine learning, it is just as applicable if a decision maker uses machine learning or other statistical techniques. That said, machine learning introduces many complications to the application of these laws, and existing law may be inadequate to address some types of discrimination that arise when machine learning is involved. At the same time, we believe that there is also an opportunity to exercise new regulatory tools to rein in algorithmic discrimination.