Tools and Weapons: The Promise and the Peril of the Digital Age

by Brad Smith

MIT researcher Joy Buolamwini, a Rhodes scholar and poet, and Stanford University researcher Timnet Gebru undertook work to advance public understanding of AI bias by comparing the facial-recognition accuracy rates for people of different genders and races. The two women documented higher error rates, for example, in identifying gender for the faces of black politicians in Africa compared to white politicians in northern Europe. As an African American woman, Buolamwini even found that some systems identified her as a man. Buolamwini and Gebru’s work helped reveal a second dimension of bias that we also need to consider. It’s difficult to build technology that serves the world without first building a team that reflects the diversity of the world. As they found, a more diverse group of researchers and engineers is more likely to recognize and think harder about bias problems that may even impact them personally.