Machine Learning Techniques for Accountability

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.