About FAT MLThe 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.”FATML.orgFATML_257a392a-836a-11ea-a0c2-33eb0a83ea00FATML PeopleSolon BarocasGeneral Chair -- Microsoft ResearchSorelle FriedlerHaverford CollegeMoritz HardtGoogleJoshua KrollCloudflareSuresh VenkatasubramanianUniversity of UtahHanna WallachMicrosoft Research and University of Massachusetts Amherst_257a3a4c-836a-11ea-a0c2-33eb0a83ea00To bring together a growing community of researchers and practitioners concerned with fairness, accountability, and transparency in machine learning_257a3b1e-836a-11ea-a0c2-33eb0a83ea00FairnessAccountabilityTransparencyVenueProvide a venue to explore how to characterize and address fairness, accountability, and transparency in machine learning_257a3bf0-836a-11ea-a0c2-33eb0a83ea00The annual event provides researchers with a venue to explore how to characterize and address these issues with computationally rigorous methods._257a3ca4-836a-11ea-a0c2-33eb0a83ea002020-04-20https://www.fatml.org/OwenAmburOwen.Ambur@verizon.net