Fairness, Accountability, and Transparency in Machine Learning (FAT ML)
Bringing 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 annual event provides researchers with a venue to explore how to characterize and address these issues with computationally rigorous methods. This will be added to Artificial Intelligence Resources Subject Tracer™. This will be added to Business Intelligence Resources Subject Tracer™. This will be added to Entrepreneurial Resources Subject Tracer™. This will be added to the tools section of Research Resources Subject Tracer™.