Part of the AI People directory: a structured reference to the individuals shaping artificial intelligence across research, industry, governance, ethics and public discourse.
Timnit Gebru, dataset ethics and AI harms, United States. Co-led Google's Ethical AI team before a high-profile departure over concerns about large language models. Co-authored the influential Stochastic Parrots paper, created datasheets for datasets, and founded the DAIR Institute for distributed AI research. *Key works: Stochastic Parrots (2021); Datasheets for Datasets; the DAIR Institute.*
Joy Buolamwini, algorithmic auditing and facial recognition, United States. Founded the Algorithmic Justice League and conducted landmark audits showing facial recognition systems performed worse on darker-skinned faces. Her Gender Shades research led companies and governments to reconsider facial-recognition deployment. *Key works: Gender Shades research; Unmasking AI (2023).*
Kate Crawford, political economy of AI, United States. Senior researcher whose Atlas of AI examines AI's material infrastructure, from mining to labour to environmental impact, situating AI within broader power structures rather than treating it as purely technical. *Key works: Atlas of AI (2021); the AI Now Institute.*
Emily M. Bender, linguistics and LLM critique, United States. Computational linguist who co-authored the Stochastic Parrots paper critiquing large language models' costs and limitations, and advocates for rigorous documentation and grounded claims about what AI systems actually do. *Key works: Stochastic Parrots (2021); language-model critique.*
Ruha Benjamin, race and technology, United States. Sociologist whose Race After Technology examines how algorithms encode and amplify racial discrimination, reframing AI bias around systemic racism rather than technical fixes with her concept of the New Jim Code. *Key works: Race After Technology (2019).*
Safiya Umoja Noble, search bias and information systems, United States. Author of Algorithms of Oppression, which documented how search engines reinforce racist and sexist stereotypes, influencing how researchers and regulators think about algorithmic harms. *Key works: Algorithms of Oppression (2018).*
Virginia Eubanks, automated inequality, United States. Author of Automating Inequality, which investigated how automated decision systems in welfare, housing and criminal justice harm vulnerable populations, bringing algorithmic accountability to broader public attention. *Key works: Automating Inequality (2018).*
Meredith Whittaker, AI accountability and organisational power, United States. Co-founded the AI Now Institute and led Google employee organising against military AI contracts, and is now president of Signal, connecting AI accountability to broader questions of corporate and state power. *Key works: the AI Now Institute; Signal leadership.*
Margaret Mitchell, model documentation, United States. Created Model Cards, standardised documentation for machine learning models now widely adopted, providing practical tools for improving transparency and accountability. *Key works: Model Cards; responsible AI practices.*
Arvind Narayanan, AI snake oil and accountability, United States. Princeton researcher whose AI Snake Oil analysis distinguishes genuine AI capabilities from overhyped claims, helping organisations and policymakers separate substance from marketing. *Key works: AI Snake Oil research and book.*
Cathy O'Neil, algorithmic accountability, United States. Author of Weapons of Math Destruction, which brought algorithmic harms to mainstream attention by documenting how models in hiring, lending and policing encode bias, and whose consulting practice audits algorithms for discrimination. *Key works: Weapons of Math Destruction (2016).*
Luciano Floridi, information ethics and philosophy of AI, United Kingdom. Philosopher who developed theoretical frameworks for information ethics and AI ethics, providing conceptual foundations that inform governance approaches. *Key works: philosophy of information; AI ethics frameworks.*