Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification

Timnit Gebru and Joy Buolamwini. "Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification." In Proceedings of the 1st Conference on Fairness, Accountability, and Transparency, edited by Sorelle A. Friedler and Christo Wilson, 77-91. New York: PMLR, 2018.

Summary: This research paper investigates the biases and inaccuracies in commercial gender classification algorithms. It highlights the intersectional disparities and implications of algorithmic bias. The paper will serve as a case study for understanding the issues of bias and fairness in AI systems.