AI in Education: Bias Impact on Students of Color - Invisibility in Digital Learning

Recent incidents have highlighted the challenges faced by students of color in digital learning environments due to AI systems misidentifying or failing to recognize them. These issues, stemming from inherent biases within the algorithms, undermine the fairness and effectiveness of such technology.

Matched TAIM controls

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

GOVERN 3.1 — similarity 0.546, rank 1. TAIM detail and related incidents →
MAP 1.6 — similarity 0.537, rank 2. TAIM detail and related incidents →
GOVERN 4.3 — similarity 0.534, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/138

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.