Unmasking AI: My Mission to Protect What Is Human in a World of Machines

by Joy Buolamwini

The classification systems I or other machine learning practitioners select, modify, inherit, or expand to label a dataset are a reflection of subjective goals, observations, and understandings of the world. These systems of labeling circumscribe the world of possibilities and experience for a machine learning model, which is also limited by the data available. For example, if you decide to use binary gender labels—male and female—and use them on a dataset that includes only the faces of middle-aged white actors, the system is precluded from learning about intersex, trans, or nonbinary representations and will be less equipped to handle faces that fall outside its initial binary training set. The classification system erases the existence of those groups not included in it. It can also reify the groups so that if the most dominant classification of gender is presented in the binary male and female categorization, over time that binary categorization becomes accepted as “truth.” This “truth” ignores rich histories and observations from all over the world regarding gender that acknowledge third-gender individuals or more fluid gender relationships. Two-spirit people have long been recognized in Native American cultures. Hijra or Kinnar people in India can include transgender and intersex individuals.