Joy Buolamwini, a PhD candidate at MIT Media Lab at the time, created an ‘artificial intelligence’ (AI) app called ‘Aspire Mirror’ which functions like a mirror.
She would look in the ‘mirror’ and view inspiring images projected on her face. She noted immediately that when she put on a white mask it would be detected by the app. However, when she used her own African American face, the image would not be detected. This discovery put Buolamwini on a path of activism devoting herself to the worldwide adverse impacts of built-in bias in ubiquitous computer algorithms.
Two examples of algorithmic failure are: Apple co-founder Steve Wozniak discovered that he automatically received 10 times the credit limit that his wife received despite the fact that they share bank accounts, credit accounts, and assets. A much more dangerous failure occurred when, in 1983, a Soviet algorithm almost brought about a global nuclear catastrophe.
Shalini Kantayya’s Coded Bias is an exploration of the impacts of bias in computer algorithms—also called ‘data centric technology’ or ‘algorithmic determinism.’ Buolamwini is the star of this show. She appears throughout the film telling her story along with a host of experts about the destructive impacts of AI bias.
One of those impacts is ‘Risk assessment,’ a major category of algorithms designed to make decisions regarding college admissions, credit and loan applications, job applications/hiring, and criminal sentencing, recidivism, parole, and probation decisions. Lives can be shattered by faulty or biased algorithms. The film also addresses the dangerous use of facial recognition algorithms which sabotage civil rights.
A Rhodes Scholar and Fulbright Fellow, Buolamwini has addressed Congress, founded the Algorithmic Justice League, and created a personal website called: Poet of Code. She has been on several notable lists, and Fortune magazine named her “the conscience of the AI revolution.”
Coded Bias is a finely crafted documentary that exposes the damage done by biased computer algorithms to the fabric of global society, and points to crucial efforts to bring about social justice by removing built-in algorithmic bias.