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.
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.
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.