Joy Buolamwini
Founder · Algorithmic Justice League
Showed that commercial face analysis failed more often on darker-skinned women, and built a public practice of auditing systems that claim to see us.
Voices
Who to read in each topic
Not a ranking. People and groups whose public work helps you go deeper. Save a piece to review later, or open it in a new tab.
Who is harmed, who is excluded, and whose dignity is diminished in outputs and decisions.What is known about people, who knows it, and what that knowledge is used for.Shared facts, persuasion, elections, and the conversations a public needs.Fraud, cybercrime, and the weaponization of capable systems.What happens between a person and a system: overreliance, loneliness, lost judgement.Care, clinics, and the people who depend on systems they did not choose.Work, wages, skill, and the meaning of contributing.What is learned, what is lost, and who gets to know.Who can use these systems, who is shut out, and who captures the gains.Language, culture, meaning, and the texture of a shared life.
Timnit Gebru
Founder · Distributed AI Research Institute
Named the labour, environmental, and stereotyping costs of large language models, and built an independent lab after leaving a major lab.
Ruha Benjamin
Professor of African American studies · Princeton
Race After Technology is the book that lets a group see how ranking and scoring repeat older patterns of who is trusted.
Safiya Umoja Noble
Professor · UCLA
Algorithms of Oppression showed how search rankings can encode harm. A starting point for culture, identity, and what a system 'knows' about people.
Cathy O'Neil
Mathematician and writer
Weapons of Math Destruction is still the plainest account of scoring systems that punish the people least able to contest them.
Inioluwa Deborah Raji
Researcher · UC Berkeley
Wrote the case for closing the AI accountability gap: documentation, audit, and the right to know what a system was tested on.
Abeba Birhane
Cognitive scientist · Trinity College Dublin · Mozilla
Writes about relational ethics and the colonial patterns that hide in datasets. A voice for culture, identity, and who is made into data.