The Frontiers of Fairness in Machine Learning

Alexandra Chouldechova, Aaron Roth
[arXiv]

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.

This is a report resulting from the CCC workshop on Fair Representations and Fair Interactive Learning that we ran in March 2018, in Philadelphia.