Algorithmic Justice

MIT Media Lab researcher Joy Buolamwini SM ’17 created the Gender Shades project to examine error rates in the gender classification systems of three commercially available facial-analysis products. Her accompanying paper shows a significant accuracy gap between classifying male and female faces, as well as between darker and lighter faces. One gap was most pronounced: the highest error for light-skinned males was .08% while, for darker females, it was 34.7%—raising questions about the data sets used to train such machine learning systems. Buolamwini is founder of the Algorithmic Justice League, devoted to highlighting algorithmic bias and developing practices of accountability during the design, development, and deployment of coded systems.