Predictive Policing Biases of PredPol

November 18, 2015

Predictive policing algorithms, such as PredPol, aim to support law enforcement by forecasting potential crime hotspots. However, recent findings suggest these tools may exhibit biased results.

Learn how Project Cerebellum's AI governance efforts strive to establish trustworthy and unbiased predictive policing systems for safer communities. Join us and help drive the conversation on safe and secure AI practices. JOIN US

Understand how incidents like this map to HISPI Project Cerebellum TAIM (Govern) by exploring our AI incident database.

Matched TAIM controls

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

MEASURE 2.6 — similarity 0.687, rank 1. TAIM detail and related incidents →
MEASURE 3.1 — similarity 0.675, rank 2. TAIM detail and related incidents →
MAP 5.1 — similarity 0.660, rank 3. TAIM detail and related incidents →

Alleged deployer: predpol, oakland-police-department
Alleged developer: predpol
Alleged harmed parties: oakland-residents

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.