Assessing the Fairness and Boundaries of Recidivism Prediction Models

Delving into the intricacies of AI, we examine a critical application: predicting recidivism. While promising in crime reduction efforts, these models must meet rigorous standards for accuracy and fairness to uphold trustworthy AI principles. This analysis highlights the potential pitfalls and offers insights on responsible AI governance.

Matched TAIM controls

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

MEASURE 2.11 — similarity 0.595, rank 1. TAIM detail and related incidents →
MEASURE 1.3 — similarity 0.575, rank 2. TAIM detail and related incidents →
MAP 1.5 — similarity 0.574, 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.