The Too Accurate Algorithm
Much research on the law and policy of algorithms has focused on ways to detect or prevent algorithmic misbehavior or mistake. However, there are also problems that result when algorithms perform their assigned tasks too well rather than too poorly. This presentation makes the case that significant individual harms and social welfare losses alike can and do occur in the face of the ever more common phenomenon of the too accurate algorithm. The recognition of algorithmic performance as a factor that can produce harm is an essential step towards better design of and regulation for algorithmically-driven technologies. Technologists and lawmakers alike should account for such accuracy harms in ongoing reform efforts to protect consumers and other vulnerable decision subjects in our increasingly algorithmic society.
Aileen Nielsen is a Ph.D. Student at the Center for Law & Economics and joined as a Fellow in Law & Tech, following time spent practicing law in New York City and pursuing graduate studies in solid state physics. Aileen has also worked at a variety of tech startups, including healthcare and political organizations. She holds a J.D. from Yale Law School and a B.A. from Princeton University.
Aileen studies regulatory and judicial responses to technological innovation with both empirical and experimental methods. She is particularly interested in the emerging development of regulatory infrastructure for data-driven AI and its enabling devices.