Police Reportedly Deployed ShotSpotter Sensors Disproportionately in Neighborhoods of Color

May 4, 2012

The placement of ShotSpotter sensors, an AI-powered crime detection tool, has been disproportionately concentrated in black and brown neighborhoods. This practice raises concerns over the creation of potentially hazardous situations, as exemplified by the unfortunate case involving Adam Toledo. Ensuring safe and secure AI practices is a crucial aspect of Project Cerebellum's mission for responsible AI governance.

For those interested in shaping the future of AI incident response and management, we invite you to explore the HISPI Project Cerebellum TAIM (Govern) function.

Matched TAIM controls

Suggested mapping from embedding similarity (not a formal assessment). Browse all TAIM controls

MEASURE 2.6 — similarity 0.704, rank 1. TAIM detail and related incidents →
MAP 4.1 — similarity 0.686, rank 2. TAIM detail and related incidents →
MAP 1.6 — similarity 0.683, rank 3. TAIM detail and related incidents →

Alleged deployer: kansas-city-police-department, cleveland-division-of-police, chicago-police-department, atlanta-police-department
Alleged developer: shotspotter
Alleged harmed parties: neighborhoods-of-color, brown-communities, black-communities, adam-toledo

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/257

Data source

Incident data is from the AI Incident Database (AIID).

When citing the database as a whole, please use:

McGregor, S. (2021) Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database. In Proceedings of the Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21). Virtual Conference.

Pre-print on arXiv · Database snapshots & citation guide

We use weekly snapshots of the AIID for stable reference. For the official suggested citation of a specific incident, use the “Cite this incident” link on each incident page.