Chicago Strategic Subject List Algorithm Disproportionately Targeted Black Neighborhoods

The Chicago Police Department implemented the Strategic Subject List (SSL) algorithm in 2013 as part of its predictive policing initiative. Developed by Illinois Institute of Technology researcher Miles Wernick, the algorithm assigned risk scores from 1 to 500 to individuals based on their arrest history, associates, and other factors to predict their likelihood of being involved in violent crime as either perpetrator or victim. The RAND Corporation's 2016 evaluation revealed severe racial disparities in the algorithm's targeting. While Black residents comprised 33% of Chicago's population, they represented 56% of those on the SSL. Latino residents, making up 29% of the city's population, accounted for 23% of the list. The algorithm essentially codified and amplified existing racial biases in Chicago's policing data, creating a technological justification for disproportionate surveillance and intervention. More troubling, RAND found that individuals on the SSL were significantly more likely to be shot by police during encounters, while the algorithm showed no measurable impact on reducing gun violence or overall crime rates. The study analyzed data from 398,000 individuals and concluded that the tool failed its primary stated objective of violence prevention while creating substantial harm through discriminatory targeting. Civil rights organizations, including the ACLU of Illinois, filed multiple lawsuits challenging the SSL as a violation of constitutional equal protection rights. They argued the algorithm created a digital stop-and-frisk program that systematically violated Fourth and Fourteenth Amendment protections. Community advocates documented cases where individuals were repeatedly stopped and harassed by police solely based on their SSL scores. The controversy intensified as journalists and researchers revealed the algorithm's lack of transparency and accountability mechanisms. Police officers received SSL scores without context about the methodology or error rates, leading to decisions based on algorithmic recommendations that subjects could not challenge or understand. The Chicago Police Department initially defended the program but faced mounting evidence of its discriminatory impact and ineffectiveness. Facing sustained legal challenges, community pressure, and academic criticism, the Chicago Police Department officially discontinued the SSL program in 2019. The incident became a landmark case study in algorithmic bias in criminal justice systems and influenced policy discussions about AI governance in law enforcement nationwide.