Abstract: Discriminatory bias in algorithmic systems is widely documented. How should the law respond? A clear consensus suggests the lens of indirect discrimination, focusing on algorithmic systems’ impact. In this talk, we set out to challenge this approach, arguing that it is both normatively undesirable and built on an unduly narrow understanding of direct discrimination, particularly in the context of machine learning systems. We illustrate how certain forms of algorithmic bias in frequently deployed algorithms might constitute direct discrimination, and explore the ramifications – not least, the absence of proportionate justifications for algorithmic discrimination. (This is a joint work of Jeremias Adams-Prassl, Reuben Binns, Aislinn Kelly-Lyth.)
Speaker Bio: Reuben Binns is an Associate Professor of Human Centred Computing, working between computer science, law, and philosophy, focusing on data protection, machine learning, and the regulation of and by technology. Between 2018-2020, he was a Postdoctoral Research Fellow in AI at the Information Commissioner's Office, addressing AI / ML and data protection. He joined the Department of Computer Science at the University of Oxford as a postdoctoral researcher in 2015. He received his Ph.D. in Web Science from The University of Southampton in 2015.