Machine learning is increasingly part of our everyday lives, influencing not only our individual interactions with online websites and platforms, but even national policy decisions that shape society at large. When algorithms make automated decisions that can affect our lives so profoundly, how do we make sure that their decisions are fair, verifiable, and accountable? This workshop will explore how to integrate these concerns into machine learning and how to address them with computationally rigorous methods.
The workshop takes place at an important moment. The debate about ‘big data' on both sides of the Atlantic has begun to expand beyond issues of privacy and data protection. Policymakers, regulators, and advocates have recently expressed fears about the potentially discriminatory impact of analytics, with many calling for further technical research into the dangers of inadvertently encoding bias into automated decisions. At the same time, there is growing alarm that the complexity of machine learning may reduce the justification for consequential decisions to “the algorithm made me do it”. Decision procedures perceived as fundamentally inscrutable have drawn special attention.
The workshop will bring together an interdisciplinary group of researchers to address these challenges head-on.