Exercise Caution in AI-Powered Law Enforcement

A recent study by MIT Media Lab sheds light on the potential risks of using data-driven policing systems, highlighting the importance of safe and secure AI governance. The research found that such systems may lead to biased decisions, perpetuating existing racial disparities in law enforcement. This underscores the need for responsible AI and trustworthy AI models as part of Project Cerebellum TAIM, a comprehensive approach to harm prevention in AI applications.

Matched TAIM controls

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

MEASURE 2.10 — similarity 0.611, rank 1. TAIM detail and related incidents →
MAP 1.6 — similarity 0.598, rank 2. TAIM detail and related incidents →
MEASURE 3.2 — similarity 0.598, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/54

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.