Algorithmic Teacher Evaluation Program Falls Short on Student Outcomes Goals and Potentially Causes Harm: A Case Study in Responsible AI
September 1, 2009
The Gates Foundation-funded Intensive Partnerships for Effective Teaching Initiative's algorithmic teacher performance assessment program, aimed at improving student outcomes, faced criticism for its poor performance, particularly among minority students. This AI incident raises concerns about the potential harm it caused to teachers and highlights the need for safe, trustworthy, and responsible AI. Ready to help shape responsible AI? JOIN US This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM).
- Alleged deployer
- intensive-partnerships-for-effective-teaching
- Alleged developer
- intensive-partnerships-for-effective-teaching
- Alleged harmed parties
- students, low-income-minority-students, teachers
Source
Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/239
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.