Timnit discusses the complexities of defining fairness in AI, highlighting the challenges of using social constructs like race and gender for data annotation. She emphasizes the need for intersectional testing to ensure models perform equitably across diverse subgroups, while also acknowledging the ethical implications of gathering data from minority communities. The conversation sheds light on the evolving discourse around fairness and the importance of critically engaging with these concepts in AI research.
