AI incident ·
ShotSpotter Failed to Alert Authorities of Mass Shooting in North Carolina
In brief
An AI system built by ShotSpotter and deployed by Law enforcement and Durham Police Department allegedly harmed mass shooting victims, Law enforcement and 2 others.
- Risk domain
- AI system safety, failures, and limitations
- Occurred
- Coverage
- 5 reports
What happened
ShotSpotter did not detect gunshots and alert Durham police of a drive-by shooting in Durham, North Carolina which left five people in hospital on New Year's Day.
Laws that address this harm
Policy angle: Classified under AI system safety, failures, and limitations (Lack of capability or robustness) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.
- India DPDP Act
- Law on Artificial Intelligence (2025)
- Law No. 132/2025 on artificial intelligence
- EU AI Act
- Texas Responsible AI Governance Act (TRAIGA)
Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).
News reports (5)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
- Alleged deployer
- Law enforcement Durham Police Department
- Alleged developer
- ShotSpotter
- Alleged harmed party
- mass shooting victims Law enforcement Durham residents Durham Police Department
AI systems implicated
Classification (MIT AI Risk Repository taxonomy)
- Risk domain
- AI system safety, failures, and limitations
- Risk subdomain
- 7.3 Lack of capability or robustness
- Causal entity
- AI
- Intent
- Unintentional
- Timing
- Post-deployment
- Harm level
- —
- Sectors
- —
- Countries
- —
Risk entries describing this failure mode
Entries from the MIT AI Risk Repository coded to subdomain 7.3.
- Reliability issues
"Relying on general-purpose AI products that fail to fulfil their intended function can lead to harm. For example, general- purpose AI systems can make up facts (‘hallucination’), generate erroneous computer code, or pro...
- Type 2: Bigger than expected
Harm can result from AI that was not expected to have a large impact at all, such as a lab leak, a surprisingly addictive open-source product, or an unexpected repurposing of a research prototype.
- Type 3: Worse than expected
AI intended to have a large societal impact can turn out harmful by mistake, such as a popular product that creates problems and partially solves them only for its users.
- Ethics and Morality Issues
LMs need to pay more attention to universally accepted societal values at the level of ethics and morality, including the judgement of right and wrong, and its relationship with social norms and laws.
- Safe learning
"AGIs should avoid making fatal mistakes during the learning phase. Subproblems include safe exploration and distributional shift (DeepMind, OpenAI), and continual learning (Berkeley)."
- Malign belief distributions
"Christiano (2016) argues that the universal distribution M (Hutter, 2005; Solomonoff, 1964a,b, 1978) is malign. The argument is somewhat intricate, and is based on the idea that a hypothesis about the world often includ...
- Meta-cognition
"Agents that reason about their own computational resources and logically uncertain events can encounter strange paradoxes due to Godelian limitations (Fallenstein and Soares, 2015; Soares and Fallenstein, 2014, 2017) an...
- Technical and operational risks
"To date, technical limitations and vulnerabilities are present in most generative AI models in various contexts. Consequently, malicious users find it easier to breach an AI system’s safety and ethical guardrails to e...
Related incidents
Linked by editors or by text similarity in the source dataset.
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Other incidents involving Law enforcement
- Deputies in Cherokee County, Georgia, Allegedly Misused Automated License Plate Reader Data for Non-Law-Enforcement Purposes
- Border Patrol Agent Allegedly Claimed Facial Recognition Identified Minneapolis ICE Observer and Global Entry Was Reportedly Revoked Three Days Later
- West Midlands Police Reportedly Relied on Erroneous Copilot-Generated Intelligence in Maccabi Tel Aviv Away-Fan Ban Decision
- ICE Facial Recognition App Mobile Fortify Reportedly Misidentified Woman Twice During Immigration Enforcement in Oregon
- Purported Facial Recognition Error Reportedly Led to Arrest and Monthslong Jailing of Tennessee Woman in North Dakota Fraud Case
- New Orleans Police Reportedly Used Real-Time Facial Recognition Alerts Supplied by Project NOLA Despite Local Ordinance
Source record: incident #446 on the AI Incident Database · all 5 reports