Empirical audit and expose of deployed AI systems

Run a quantitative test on a deployed AI system — face recognition, credit scoring, content moderation, generative model behaviour — and publish the disparity numbers as an attributable, citable record. The audit is engineered so the result is hard to dispute: a stratified sample, a clear metric, a methodology section other researchers can replicate. The publication is timed for press coverage and routed at journalists, regulators, and the deploying organisation in parallel.

An organisation chooses this strategy because deployed AI systems are evidence-poor by default — the operator controls disclosure, and aggregated statistics from impact reports do not survive contact with a serious sample. A reproducible audit converts a movement claim ("this system is biased") into a citation other actors can carry: into legislation, into court filings, into a procurement officer's decision not to buy. Once a number is in the public record, every later defence of the system has to argue against it.

The trade-off is the cost of doing the science well — bad audits poison the well — and the structural problem that auditing is easier to fund than fixing: a movement can accumulate empirical exposures faster than the political and legal apparatus around it can convert them into outcomes.