via TED Radio Hour
Jan. 26, 2018
Buolamwini, J. (2017, MIT Master's Thesis) Gender Shades: Intersectional Phenotypic and Demographic Evaluation of Face Datasets and Gender Classifiers
Buolamwini, J., Gebru, T. "Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification." Proceedings of Machine Learning Research 81:1–15, 2018 Conference on Fairness, Accountability, and Transparency
Real-world biases and artificial intelligence
Examination of facial-analysis software shows error rate of 0.8 percent for light-skinned men, 34.7 percent for dark-skinned women.