The analysis, published this week in the proceedings of the Association of Computing Machinery Conference on Fairness, Accountability and Transparency(link is external), evaluates machine learning techniques designed to support a U.S. Environmental Protection Agency (EPA) initiative to reduce severe violations of the Clean Water Act. It reveals how two key elements of so-called algorithmic design influence which communities are targeted for compliance efforts and, consequently, who bears the burden of pollution violations. The analysis -- funded through the Stanford Woods Institute for the Environment's Realizing Environmental Innovation Program -- is timely given recent executive actions(link is external) calling for renewed focus on environmental justice. "Machine learning is being used to help manage an overwhelming number of things that federal agencies are tasked to do -- as a way to help increase efficiency," said study co-principal investigator Daniel Ho, the William Benjamin Scott and Luna M. Scott Professor of Law at Stanford Law School. "Yet what we also show is that simply designing a machine learning-based system can have an additional benefit."