A Popular Algorithm Is No Better at Predicting Crimes Than Random People

An analysis of a widely-used algorithm in crime prediction has revealed its performance is on par with random human predictions, casting doubts on its reliability and efficacy. The study emphasizes the need for responsible AI governance and trustworthy models in ensuring safe and secure AI applications.

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MEASURE 3.2 — similarity 0.531, rank 1. TAIM detail and related incidents →
MAP 5.1 — similarity 0.525, rank 2. TAIM detail and related incidents →
MEASURE 2.10 — similarity 0.523, rank 3. TAIM detail and related incidents →

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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.