AI's Limitations in Identifying Recidivism: Ineffective Predictions by Algorithms

A recent study has uncovered a surprising fact - algorithms used to predict repeat offenders are no more accurate than inexperienced humans. This revelation underscores the need for responsible AI governance and trustworthy AI models, ensuring safe and secure practices in our increasingly automated world. HISPI Project Cerebellum TAIM invites contributors through JOIN US to learn more about harm prevention and the importance of guardrails for AI.

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

MEASURE 2.6 — similarity 0.547, rank 1. TAIM detail and related incidents →
MEASURE 3.2 — similarity 0.544, rank 2. TAIM detail and related incidents →
MEASURE 2.5 — similarity 0.543, 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.