FAT M/L: 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 FAT/ML event provides researchers with a venue to explore how to characterize and address these issues with computationally rigorous methods.

Company Type: Nonprofit

Region: Europe

Industry Category: Technology

Fighting Type of Bias: Algorithmic bias Data-driven bias

Research: Papers from Annual 2018 Conference