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Researchers warn police AI may deepen bias from crime records
BBC reports researchers say predictive policing tools trained on biased records can magnify discrimination unless independently overseen.
Researchers warn that police use of artificial intelligence to predict crime can deepen discrimination against minority groups when systems learn from biased records, according to a BBC report of an award-winning science-writing essay published Sunday1.
Studies cited in the piece say hotspot forecasts can rest on skewed data, send more officers to the same places, record more crime there, and then treat that extra recording as proof the area is riskier1.
Dr. Carolyn McKay, an associate professor in digital justice at the University of Sydney Law School, said forces should review the data they feed these tools and remove bias, with people still checking the systems1. "The public functions of state policing ought to be subject to human oversight and scrutiny," McKay said1.
Sarah Brayne, a sociologist at the University of Texas at Austin, studied the Los Angeles Police Department's use of big data and reported in the American Sociological Review that the force increasingly used data to predict crime rather than only respond to it1. She found public-place offenses were more likely to be recorded and that police focus fell disproportionately on Black neighborhoods, so later predictions could look impartial while repeating that pattern1.
New York University researchers described some police records as "dirty data" shaped by corrupt, biased or unlawful practices, including rigged figures and arrests of innocent people1. In nine of 13 U.S. areas they examined, data from periods of unlawful or biased policing was available to train such systems, they said1.
Dr. Dimitrios Kollias, an associate professor in multimodal AI at Queen Mary University of London, told BBC News that modern systems do not only copy training examples and that bias is not inevitable if systems are tested, data and objectives improved, and safeguards added1. He also said more autonomous tools still need to stay under meaningful human control1.
The BBC piece is an edited version of Adrian Dredge's winning 2026 Young Science Writer Award essay for the Association of British Science Writers, not a new peer-reviewed study of its own1. It also recounts a 2019 Science study of a U.S. health-care algorithm that used cost as a proxy for need and under-selected Black patients until researchers changed the objective1.