Chicago police Heat List criticized for racial bias and ineffectiveness
The Chicago Police Department's Strategic Subject List (SSL), known as the Heat List, was designed to predict individuals likely to be involved in shootings. Independent analysis by Upturn and the RAND Corporation found the system was ineffective at reducing violence and disproportionately targeted individuals based on age and systemic bias.
Age accounts for roughly 89% of variance in SSL scores.
Key facts
- What
- The Chicago Police Department's Strategic Subject List (SSL), known as the Heat List, was designed to predict individuals likely to be involved in shootings.
- Incident date
- Jan 1, 2016
- Who
- Chicago Police Department
- Failure mode
- Policy Violation
- AI surface
- Algorithmic Decision
- Severity
- High
What happened
The Chicago Police Department implemented the Strategic Subject List to identify residents most likely to be involved in shootings. The system assigned risk scores to individuals, leading to heightened police scrutiny and 'custom notification' visits for those flagged as high risk. Independent research found the program was ineffective in reducing gun violence.
What broke inside the model
- 01 · TriggerA prompt pushes against a deployment boundary.
- 02 · Model stepThe model produces the disallowed output.
- 03 · Control gapNo enforcement blocks it at generation time.
- 04 · FailureThe output crosses the policy line.
- 05 · ConsequenceA limit the business set is breached in public.
The output crosses a policy boundary the deployment had defined.
The model's risk scores were predominantly driven by the age of the individual, which accounted for approximately 89% of the variance in scores. This mechanism effectively rank-ordered younger residents as higher risk, reproducing systemic social patterns rather than accurately predicting individual criminal behavior.
What it cost
Sources
- PressHow strategic is Chicago's Strategic Subjects List?upturn.org
- PressCPD's 'Heat List' and the Dilemma of Predictive Policingrand.org
Cite this entry
https://failureindex.ai/failures/chicago-police-department-heat-list-reinforcedAI Failure Index. "Chicago police Heat List criticized for racial bias and ineffectiveness" (FI-0332). Realm Labs. https://failureindex.ai/failures/chicago-police-department-heat-list-reinforced (indexed Jun 9, 2026).Data fields CC-BY 4.0, prose citation permitted. Incident ID FI-0332. Full dataset at /data.
Note from Realm Labs, the Index steward
How Realm fits
- Prism
- OmniGuard
This entry sits in the index's predictive wing: a system that scores, ranks, perceives, or steers rather than generates. Realm's runtime layer is built for the generative and agentic systems now moving into these same decision seats, where it watches a model's internal state and holds an unsupported claim or an unchecked action before it commits. The control gap on this record, an automated decision that reached people with no runtime check in front of it, is the same gap. The index keeps predictive failures on the record because the pattern carries straight into the systems shipping today.