(How) Does Accountability Require Understanding ML Models?

Concerns about artificial intelligence—particularly systems based on machine learning models whose internal operations are opaque to human understanding—are frequently framed as concerns about accountability. Yet the concept of accountability itself is often left underspecified, encompassing a heterogeneous set of issues that call for distinct responses. This paper focuses on a specific subset of accountability concerns: the “who” questions. We distinguish between two questions: When an AI system causes harm, who is to blame, and who bears the obligation to provide compensation? We argue that the answers to these two questions about accountability require different information and do not entail one another. Then we investigate whether the opacity of contemporary machine learning models undermines our capacity to hold the relevant actors accountable in these two different senses of accountability, and how to rethink efforts aimed at increasing understanding.