Buolamwini is a computer scientist, founder of the Algorithmic Justice League and a poet of code.  Machines can discriminate in harmful ways. I experienced this firsthand, when I was a graduate student at MIT in 2015 and discovered that some facial analysis software couldn’t detect my dark-skinned face until I put on a white mask. These systems are often trained on images of predominantly light-skinned men. And so, I decided to share my experience of the coded gaze, the bias in artificial intelligence that can lead to discriminatory or exclusionary practices.
After my presentation [Bias: Statistical and Significant] , last week at the [REWORK Women in AI in Healthcare Dinner] in London, I was asked if I could write something on the topic aimed at a less technical audience. It’s my hope this article will provide enough of a technical intuition about the causes of biases in algorithms, while offering an accessible take on how we are inadvertently amplifying existing social and cognitive biases through machine learning — and what we can do to stop it.