This workshop aims to bring together a growing community of researchers and practitioners concerned with fairness, accountability, and transparency in machine learning. The past few years have seen growing recognition that machine learning raises novel challenges for ensuring non-discrimination, due process, and understandability in decision-making. In particular, policymakers, regulators, and advocates have expressed fears about the potentially discriminatory impact of machine learning, with many calling for further technical research into the dangers of inadvertently encoding bias into automated decisions. At the same time, there is increasing alarm that the complexity of machine learning may reduce the justification for consequential decisions to “the algorithm made me do it.” The goal of this workshop is to provide researchers with a venue to explore how to characterize and address these issues with computationally rigorous methods.
This year, the workshop is co-located with two other highly related events: the Data Transparency Lab (DTL) Conference and the Workshop on Data and Algorithmic Transparency (DAT). We anticipate that our workshop will consist of a mix of invited talks, invited panels, and contributed talks. We welcome paper submissions that address any issue of fairness, accountability, and transparency related to machine learning, especially those that provide a bridge to empirical studies of the behavior of data-driven systems in the wild, the focus of the DTL and DAT events.