Unreliable ShotSpotter Audio Previously Used to Convict Chicago Man in Murder Case

May 31, 2020

A troubling instance of insufficient and potentially unreliable AI evidence was brought to light in a Chicago murder case. The defendant, an innocent Black man, spent nearly a year in prison based on ShotSpotter audio evidence that was later dismissed by prosecutors as insufficient. This incident underscores the importance of trustworthy AI, safe and secure practices, and Project Cerebellum's efforts towards responsible AI governance.

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Matched TAIM controls

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

MEASURE 2.6 — similarity 0.667, rank 1. TAIM detail and related incidents →
MAP 3.2 — similarity 0.628, rank 2. TAIM detail and related incidents →
MEASURE 2.5 — similarity 0.628, rank 3. TAIM detail and related incidents →

Alleged deployer: chicago-police-department
Alleged developer: shotspotter
Alleged harmed parties: michael-williams

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/255

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.