Machine Learning Techniques for Accountability
Been Kim, Finale Doshi‐Velez
Abstract
Artificial intelligence systems have provided us with many everyday conveniences. We can easily search for information across millions of web‐pages via text and voice. Paperwork processing is increasingly automated. Artificial intelligence systems flag potentially fraudulent credit‐card transactions and filter our e‐mail. Yet these artificial intelligence systems have also experienced significant failings. Across a range of applications, including loan approvals, disease severity scores, hiring algorithms, and face recognition, artificial‐intelligence‐based scoring systems have exhibited gender and racial bias. Self‐driving cars have had serious accidents. As these systems become more prevalent, it is increasingly important that we identify the best ways to keep them accountable.