This is a research prototype. The data and analyses are preliminary and not yet validated — we'd welcome your .
ShotSpotter, an acoustic gunshot detection AI system used by numerous US police departments, has been found to be highly inaccurate, frequently generating false alarms that lead to overpolicing in minority communities and wasting thousands of police hours.
ShotSpotter algorithmic systems locating gunshots were reported by police departments for containing high false positive rates and wasting police resources, prompting discontinuation.
Domain classification, causal taxonomy, severity scores, and national security assessments were LLM-classified and may contain errors.
AI systems that fail to perform reliably or effectively under varying conditions, exposing them to errors and failures that can have significant consequences, especially in critical applications or areas that require moral reasoning.
Also classified as
The system's deployment is concentrated in minority neighborhoods, resulting in disproportionate overpolicing and hostile law enforcement encounters for these groups.
Police departments rely heavily on the system's alerts, deploying officers on high alert expecting armed suspects based on unverified data.
The system fails to perform reliably under varying environmental conditions, frequently misclassifying everyday noises like fireworks and car backfires as gunshots.
AI system
Due to a decision or action made by an AI system
Unintentional
Due to an unexpected outcome from pursuing a goal
Post-deployment
Occurring after the AI model has been trained and deployed
The risks and harms arose from the AI system's failure to accurately classify sounds post-deployment, leading to unintended false alerts.
Added categories peak at 3: Substantial (Psychological Harm, Inferred)
The deployment of the ShotSpotter AI gunshot detection system has led to significant local community impacts, including overpolicing in minority neighborhoods, civil rights concerns, and wasted municipal funds due to high false-alarm rates. From a national security perspective, the incident represents a minor, localized threat to societal stability and human rights, manageable within standard domestic legal and municipal frameworks.
The AI system acts as an assistant by analyzing acoustic data to identify potential gunshots and alerting human operators and police officers, who must then verify the information and decide whether to deploy resources.
Risk Level 2: High Risk. The system is used in law enforcement applications, specifically for gunshot detection and dispatching police officers, which has significant implications for public safety and fundamental rights.
The system's primary, explicit function is to detect and locate the source of outdoor gunfire using acoustic sensors.
The reports provide extensive, detailed evidence from multiple independent audits (NYC Comptroller, MacArthur Justice Center), court testimonies, and municipal records across several cities. There is high consistency regarding the system's technical limitations, false positive rates, and social impacts, despite the manufacturer's disputed claims.
The manufacturer argues that the lack of physical evidence at alert scenes does not prove a false positive, suggesting instead that casings were not found or guns did not eject them.
Technology
Algorithmic Inaccuracy
Software struggles to differentiate gunshots from fireworks or car backfires.
Lack of Sensor Recalibration
Sensors in SF were not retested for a decade despite topography changes.
Environmental Interference
Topography, building density, and wind degrade acoustic sensor accuracy.
Data Inputs
Acoustic Noise Pollution
City noises like jackhammers and fireworks pollute the audio data streams.
Missing Sensor Locations
Exact sensor locations are withheld from police, limiting data validation.
Indoor Gunfire Exclusion
System is designed only for outdoor gunfire, failing to capture indoor shots.
Human Factors
Subjective Human Review
Reviewers have broad discretion to overrule algorithmic classifications.
Reviewer Bias and Manipulation
Reviewers modified gunshot locations and reports to suit police narratives.
Police Officer Over-reliance
Officers respond on high alert, expecting armed suspects during false alarms.
Process and Methods
Flawed Performance Metrics
Contracts measure performance using formulas that ignore false positives.
Inadequate Data Collection
Police departments fail to track officer response times and false alarm costs.
Lack of Scientific Testing
No independent, peer-reviewed studies exist to verify the 97% accuracy claim.
Management
Sales-Driven Performance Claims
The 80% accuracy guarantee was created by marketing, not engineering.
Prioritizing Growth Over Safety
Companies pushed system expansions while ignoring known accuracy issues.
Poor Fiscal Stewardship
Cities spend millions on contracts despite low arrest and conviction rates.
Regulatory Environment
Lack of Oversight and Auditing
No federal or independent regulatory standards govern gunshot detection tools.
Flawed Evidence Standards
Courts accept unverified acoustic reports as forensic evidence in trials.
Privacy Regulation Gaps
Microphones can capture private conversations up to 50 feet away.
