Analyzing Crime-Prediction AI Algorithms: A Comparison with Untrained Humans

Exploring a recent study that challenges the effectiveness of crime-predicting AI algorithms, revealing they may not outperform untrained human predictions. This discussion emphasizes the importance of responsible AI governance and trustworthy models to ensure safer, secure, and reliable AI systems.

Matched TAIM controls

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

MEASURE 3.2 — similarity 0.580, rank 1. TAIM detail and related incidents →
MEASURE 2.6 — similarity 0.578, rank 2. TAIM detail and related incidents →
MEASURE 2.5 — similarity 0.564, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/40

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.