Duplicate

April 17, 2021

In this article, we delve into a recent AI duplicate incident, discussing its implications for safe and secure AI practices. By examining the root causes and potential remedies, readers gain valuable insights on how to manage such issues. Moreover, this case aligns with the HISPI Project Cerebellum TAIM (Measure) function, contributing to our collective efforts in promoting 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 0.699, rank 1. TAIM detail and related incidents →
MEASURE 2.10 — similarity 0.680, rank 2. TAIM detail and related incidents →
MANAGE 4.3 — similarity 0.671, rank 3. TAIM detail and related incidents →

Alleged harmed parties: Duplicate

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/338

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.