Addressing Bias in AI Systems

This chapter examines a significant paper focused on mitigating bias in AI, particularly in critical areas such as criminal justice and recruitment. It discusses the effects of representational biases in natural language processing, emphasizing strategies to reduce bias without relying on protected attributes. The chapter also highlights the importance of model explainability in diagnosing biases, along with relevant developments from other academic communities.