Inaccurate Aggression Detection: High False Positive Rates in Sound Intelligence's AI Algorithm
June 25, 2019
Sound Intelligence's 'aggression detection' algorithm, implemented in schools, exhibited concerningly high false positive rates, misclassifying common sounds like laughter, coughing, cheering, and loud discussions. This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). Ready to help establish guardrails for AI? JOIN US
- Alleged deployer
- rock-hill-schools, pinecrest-academy-horizon
- Alleged developer
- sound-intelligence
- Alleged harmed parties
- students, rock-hill-school-students, pinecrest-academy-horizon-students
Source
Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/404
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.