Unveiling Human-Like Bias in Automatically Derived Semantics - A Call for Responsible AI Governance
Explore the unexpected finding of human-like biases in automatically derived semantics, shedding light on the need for robust governance in AI. This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). Stay vigilant and help us build safe and secure AI by contributing your expertise - JOIN US
Source
Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/59
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.