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.
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.
Stuart Russell
Professor of computer science · UC Berkeley · CHAI
Argues that the standard model of AI, optimizing a fixed objective, is the wrong shape, and proposes machines that remain uncertain about human preference.
Kate Crawford
Research professor · USC Annenberg
Maps AI as an extractive industry: minerals, energy, labour, and classification. Atlas of AI is the book most groups can start with.
Sasha Luccioni
Climate and AI researcher · Hugging Face
Measures the energy and carbon cost of training and serving models, so environmental claims can be compared rather than asserted.
Tristan Harris
Co-founder · Center for Humane Technology
Named how attention systems shape minds at scale, then turned the same lens on generative models in The AI Dilemma.
Renée DiResta
Writer and researcher · Formerly Stanford Internet Observatory
Traces how influence operations, rumor, and now generative media move through networks. Useful when a group wants facts about manipulation, not vibes.
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.
Bruce Schneier
Security technologist · Harvard Kennedy School
Writes about security as a social problem. A reliable first read when a group is mixing fear of hackers with fear of the model itself.
Yoshua Bengio
Scientific director · Mila · LawZero
A founder of modern deep learning who now spends his public voice on catastrophic risk, agentic systems, and international scientific advice.
Meredith Whittaker
President · Signal
Connects concentration of compute, surveillance business models, and labour. A voice for groups asking who owns the stack.
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.
Eric Topol
Cardiologist and writer · Scripps Research
Follows medical AI as a clinician who still holds the patient in view. Useful for groups in health systems who need neither hype nor refusal.
World Health Organization
Guidance on large multimodal models
Institutional guidance for health workers and ministries on generative models in care, written to be used rather than admired.
Daron Acemoglu
Economist · MIT
Power and Progress asks whether a technical gain becomes a shared one. A good first book for labour, inequality, and who captures the surplus.
Virginia Eubanks
Writer and teacher
Automating Inequality follows ranking systems in welfare, homelessness, and child protection. Read this before a civic group talks about 'efficiency.'
Gary Marcus
Scientist and writer
A persistent public critic of overclaiming. Useful when a group needs permission to ask 'does this actually work?'
Nicholas Carlini
Research scientist · Anthropic
Shows, with working attacks, what models leak and how they fail. A technical voice that still reads in a mixed group if you pick one paper.
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.
Sherry Turkle
Professor · MIT
Reclaiming Conversation is the book for groups, especially with youth, who want to talk about companionship, attention, and what a machine cannot hold.
Justin Reich
Director, Teaching Systems Lab · MIT
Failure to Disrupt is a sober history of education technology. A good companion when a school group is being sold a tutor.
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.
International Committee of the Red Cross
Humanitarian policy on AI
Writes from international humanitarian law, not from product. Essential when a conversation touches conflict, weapons, or civilian protection.
Alondra Nelson
Former deputy director, White House OSTP · Institute for Advanced Study
Shepherded the Blueprint for an AI Bill of Rights. A civic starting point for rights language without requiring a legal training.
Geoffrey Hinton
Scientist
A founder of deep learning who left a major lab to speak in public about loss of control. One interview is enough for a first gathering on long-term risk.
Future of Life Institute
Research and advocacy
Holds open letters, policy work, and explainers on catastrophic risk. Use alongside, not instead of, voices from justice and labour.
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.
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.'
Woodrow Hartzog
Professor of law · Boston University
Privacy's Blueprint argues that design, not only notice, is where privacy lives. Useful for groups writing a policy rather than a feeling.
Helen Nissenbaum
Professor · Cornell Tech
Contextual integrity: privacy is not secrecy, it is appropriate flow. The idea that lets a mixed group talk about data without collapsing into 'share nothing.'