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Incident 239: Algorithmic Teacher Evaluation Program Failed Student Outcome Goals and Allegedly Caused Harm Against Teachers

Description: Gates-Foundation-funded Intensive Partnerships for Effective Teaching Initiative’s algorithmic program to assess teacher performance reportedly failed to achieve its goals for student outcomes, particularly for minority students, and was criticized for potentially causing harm against teachers.

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Alleged: Intensive Partnerships for Effective Teaching developed and deployed an AI system, which harmed students , low-income minority students and Teachers.

Incident Stats

Incident ID
239
Report Count
1
Incident Date
2009-09-01
Editors
Khoa Lam
Applied Taxonomies
CSETv1, GMF, MIT

CSETv1 Taxonomy Classifications

Taxonomy Details

Incident Number

The number of the incident in the AI Incident Database.
 

239

MIT Taxonomy Classifications

Machine-Classified
Taxonomy Details

Risk Subdomain

A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
 

1.3. Unequal performance across groups

Risk Domain

The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
 
  1. Discrimination and Toxicity

Entity

Which, if any, entity is presented as the main cause of the risk
 

AI

Timing

The stage in the AI lifecycle at which the risk is presented as occurring
 

Post-deployment

Intent

Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
 

Unintentional

Incident Reports

Reports Timeline

Incident OccurrenceHere's How Not to Improve Public Schools
Here's How Not to Improve Public Schools

Here's How Not to Improve Public Schools

bloomberg.com

Here's How Not to Improve Public Schools
bloomberg.com · 2018

The Gates Foundation’s big-data experiment wasn’t just a failure. It did real harm.

The Gates Foundation deserves credit for hiring an independent firm to assess its $575 million program to make public-school teachers more effective. Now th…

Variants

A "variant" is an incident that shares the same causative factors, produces similar harms, and involves the same intelligent systems as a known AI incident. Rather than index variants as entirely separate incidents, we list variations of incidents under the first similar incident submitted to the database. Unlike other submission types to the incident database, variants are not required to have reporting in evidence external to the Incident Database. Learn more from the research paper.
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