Discrimination in Machine Decision Making
Krishna P. Gummadi, Max Planck Institute for Software Systems
Machine (data-driven learning-based) decision making is increasingly being used to assist or replace human decision making in a variety of domains ranging from banking (rating user credit) and recruiting (ranking applicants) to judiciary (profiling criminals) and journalism (recommending news-stories). Recently concerns have been raised about the potential for discrimination and unfairness in such machine decisions.
Against this background, in this talk, I will pose and attempt to answer the following high-level questions: (a) How do machines learn to make discriminatory decision making? (b) How can we quantify discrimination in machine decision making? (c) How can we control machine discrimination? i.e., can we design learning mechanisms that avoid discriminatory decision making? (d) Is there a cost to non-discriminatory decision making?