Joy Buolamwini
Founder of the Algorithmic Justice League
MIT researcher whose “Gender Shades” study exposed racial and gender bias in commercial facial recognition — founder of the Algorithmic Justice League and author of “Unmasking AI” (2023).
In her words
I truly believe if you have a face, you have a place in the conversation about AI.
You don’t need to have superintelligent AI systems or advanced robotics to have a real harm. A self-driving car that doesn’t see you on the road can be fatal and harmful.
Biography
Joy Buolamwini (born 1990 in Edmonton, Canada) is a Ghanaian-American-Canadian computer scientist, self-described “poet of code,” and founder of the Algorithmic Justice League. As a graduate student at the MIT Media Lab, she discovered that facial-analysis software could not detect her dark-skinned face — until she put on a white mask. She named the phenomenon the “coded gaze”: the way the priorities, preferences, and prejudices of those who build technology get baked into it.
Gender Shades and the coded gaze
Her 2018 “Gender Shades” study with Timnit Gebru audited commercial gender-classification systems from IBM, Microsoft, and Face++, finding error rates of up to 34% for darker-skinned women versus under 1% for lighter-skinned men — trained, she noted, on what she called “pale male data sets.” The paper became one of the most cited works in algorithmic-fairness research, pushed IBM and Microsoft to overhaul or retire their systems, and helped drive city bans on police facial recognition. Her story anchored the 2020 documentary “Coded Bias,” and she has testified before Congress on the technology’s harms.
Her 2023 book “Unmasking AI: My Mission to Protect What Is Human in a World of Machines” broadened the argument: she warns of the “excoded” — people harmed by AI systems that misread, exclude, or misjudge them — and argues the fixation on future superintelligence distracts from damage happening now, telling NPR that supposedly futuristic technologies are “actually taking us back from the progress already made.” Critics of her approach say bias findings on 2017-era classifiers are outdated for modern systems; her answer is that the power asymmetries that produced them have not changed.
Where they stand in the war
Who backs them up
Sources & further reading
Canonical record: https://battlelines.ai/topic/joy-buolamwini








