Jose M. Such

Bias and Discrimination in AI – Towards more transparent and explainable attribute-sensitive decisions
Abstract: 

With the widespread and pervasive use of AI for automated decision-making systems, AI bias is becoming more apparent and problematic. One of its negative consequences is discrimination: the unfair, unequal, or simply different treatment of individuals based on certain characteristics. However, the relationship between bias and discrimination is still unclear. In this talk, I will discuss current research we are conducting under the frame of an EPSRC-funded project about bias and discrimination in AI from an interdisciplinary perspective that embeds technical, legal, social and ethical dimensions. I will show that finding solutions to bias and discrimination in AI requires robust cross-disciplinary collaborations that will advance on the task of making AI more transparent and explainable to help assess whether AI systems discriminate against users and how to mitigate that.

Explainable Machine Learning-based Artificial Intelligence
June 11
Keynote talk
King's College London
Jose M. Such