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

by Joy Buolamwini

My example of coding in a white mask raised the question of whether the skew away from darker skin was also compounded by the face data collection methods themselves. To overcome this challenge, IJB-A was collected without a face detector and was curated by humans to further increase the difficulty level. Given the aim of demographic diversity with this dataset, I decided to label it to see its composition. Even with IJB-A, developed in 2015, eight years after LFW, I found that the dataset was 75.4 percent male and also 79.6 percent lighter-skinned individuals. This concerned me: Not only did we have a research gold standard that was heavily skewed, but the government agency NIST, which had set out to collect intentionally diverse datasets, also had a dataset that underrepresented women and people of color.