False Negatives for Water Quality-Associated Beach Closures

June 3, 2022

Toronto's adoption of AI predictive modeling (AIPM) for beach water quality assessments has sparked controversy over its reliability, following conflicting findings compared to traditional methods by a local water advocacy group. This incident underscores the need for trustworthy AI in critical applications such as this. For those interested in shaping the future of safe and secure AI practices, explore HISPI Project Cerebellum TAIM, where you can Govern, Map, Measure, or Manage AI incidents like this one. JOIN US

This incident highlights the importance of maintaining an AI incident database for harm prevention and establishing guardrails for AI in critical applications.

Matched TAIM controls

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

MEASURE 2.6 — similarity 0.724, rank 1. TAIM detail and related incidents →
MAP 4.1 — similarity 0.675, rank 2. TAIM detail and related incidents →
MEASURE 1.1 — similarity 0.667, rank 3. TAIM detail and related incidents →

Alleged deployer: toronto-city-government
Alleged developer: toronto-public-health
Alleged harmed parties: sunnyside-beachgoers, marie-curtis-beachgoers, toronto-citizens

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/290

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.