Work on computability, machine intelligence and morphogenesis. Begin by distinguishing a mathematical model, an operational test and a biological mechanism.

Karl Friston

Neuroscience and mathematical frameworks for inference, perception and action. Begin with a small probabilistic model before tackling the formal free-energy principle.

Claude Shannon

A mathematical account of communication under uncertainty. Separate the quantity of information in a model from the meaning or truth of a message.

Fei-Fei Li

Computer vision and human-centred approaches to AI. Study the relationship among a dataset, a benchmark and the people affected by a system.

Timnit Gebru

Research on dataset documentation, evaluation and harms in machine learning. Begin with how data was collected and which uses its documentation supports.

Ruha Benjamin

Social analysis of race and technological design. Examine how a system’s categories, defaults and institutional uses can reproduce inequality.

Kate Crawford

Research on AI’s material, labour and political conditions. Trace a system beyond its interface to extraction, human work and data infrastructures.

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What should count as success for an AI system?

Connect computation, datasets, labour and social consequences. Compare a technical evaluation with the people and purposes it leaves out.