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.
How data is gathered, whose lives it represents, and whether it can be trusted.Whether models do what they claim, how they fail, and how we know.Attacks on and through AI systems: injection, jailbreaks, cyber offense.Whether people can see, contest, and hold to account what a system does.When software acts across time and tools, faster than people can intervene.The physical and operational stack models run on — clouds, chips, outages.Energy, water, minerals, and the living world that compute depends on.
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.
Arvind Narayanan
Professor of computer science · Princeton
Cuts through AI marketing. AI Snake Oil is a calm test of which claims are real, which are premature, and which cannot work as sold.
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.
NIST AI Risk Management Framework
Voluntary framework · U.S. National Institute of Standards and Technology
The document many institutions already have to map against. Not a voice in the personal sense, but the shared language of governance conversations.
Emily M. Bender
Professor of linguistics · University of Washington
Insists that language models are not minds. A clarifying voice when a group is mixing 'the model said' with 'a person claims.'