research

Gender Shades is an excavation of inadvertent negligence that will cripple the age of automation and exacerbate inequality if left to fester.

Lessons from cybersecurity vulnerability disclosure for algorithmic harms discovery, disclosure, and redress, led by AJL researchers Josh Kenway, Camille François, Sasha Costanza-Chock, Inioluwa Deborah Raji, and Dr. Joy Buolamwini.

Although algorithmic auditing has emerged as a key strategy to expose systematic biases in software platforms, we struggle to understand the real impact of these audits.

In Facial Recognition Technologies in the Wild: A Call for a Federal Office, researchers Erik Learned-Miller, Joy Buolamwini, Vicente Ordóñez, and Jamie Morgenstern propose an FDA-inspired model that categorizes facial recognition technologies.

Facial Recognition Technologies: A Primer provides a basic introduction to the terminology, applications, and difficulties of evaluating this complex set of technologies.