Keynote Speech 2: Bias and Discrimination in AI Systems: From Single-Identity Dimensions to Multi-Discrimination.

AI-driven decision-making has become pervasive in various aspects of our lives, impacting everyone, everywhere, at any time. However, concerns have arisen regarding the discriminatory effects of AI, as evidenced across diverse domains like content recommendation, healthcare, predictive policing, and autonomous driving. The field of fairness-aware machine learning aims to address bias and discrimination in AI/ML models, but most existing approaches focus on discrimination related to a single protected attribute, such as gender or race. In reality, human identities are multi-dimensional, and discrimination can arise from multiple protected characteristics, for example a combination of gender, race and age. In this talk, I will explore fairness and discrimination in supervised learning for tabular data, progressing from traditional single-attribute discrimination to addressing the complexities of multi-dimensional discrimination.