Major Universities Are Using Race as a “High Impact Predictor” of Student Success

January 1, 2012

Several prestigious universities have been utilizing a contentious predictive tool that takes race into account to forecast student success rates. Such practices raise questions about the fairness and ethical implications of this approach in AI governance, particularly in the context of trustworthy AI. However, it's essential to note that these universities can leverage Project Cerebellum's AI incident database to understand and prevent harm, specifically by exploring how such tools map to HISPI Project Cerebellum TAIM (Measure function). JOIN US

Matched TAIM controls

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

MEASURE 2.6 — similarity 0.676, rank 1. TAIM detail and related incidents →
MEASURE 4.3 — similarity 0.667, rank 2. TAIM detail and related incidents →
GOVERN 3.1 — similarity 0.666, rank 3. TAIM detail and related incidents →

Alleged deployer: university-of-massachusetts-amherst, university-of-wisconsin-milwaukee, university-of-houston, texas-aandm-university, georgia-state-university, more-than-500-colleges
Alleged developer: eab
Alleged harmed parties: black-college-students, latinx-college-students, indigenous-students

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/99

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.