The Frontiers of Fairness in Machine Learning
It addresses the foundational problem of fairness in AI for researchers and practitioners, but is incremental as it consolidates existing knowledge rather than presenting new breakthroughs.
The report summarizes a 2018 workshop that assessed the nascent state of algorithmic fairness in machine learning, identifying key research directions and surveying recent theoretical work to guide future efforts.
The last few years have seen an explosion of academic and popular interest in algorithmic fairness. Despite this interest and the volume and velocity of work that has been produced recently, the fundamental science of fairness in machine learning is still in a nascent state. In March 2018, we convened a group of experts as part of a CCC visioning workshop to assess the state of the field, and distill the most promising research directions going forward. This report summarizes the findings of that workshop. Along the way, it surveys recent theoretical work in the field and points towards promising directions for research.