Examining AI Models for Fairness: A Crucial Step Towards Responsible AI

In the rapidly evolving landscape of Artificial Intelligence, ensuring fairness has emerged as a key concern. This article sheds light on the importance of inspecting algorithms for potential bias, a practice that serves as a crucial guardrail in our pursuit of safe and secure AI. Understanding and addressing these issues is essential for building trustworthy AI models, contributing to the growth of the HISPI Project Cerebellum TAIM. Through JOIN US, join us to learn more about AI governance and harm prevention.

Matched TAIM controls

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

MEASURE 2.11 — similarity 0.635, rank 1. TAIM detail and related incidents →
MEASURE 2.10 — similarity 0.571, rank 2. TAIM detail and related incidents →
MEASURE 1.1 — similarity 0.569, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/40

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.