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Algorithmic Teacher Evaluation Program Reportedly Failed to Improve Student Outcomes and Was Alleged to Have Harmed Teachers

1 news report Synced from source · record last edited 5 Sep 2026

In brief

An AI system built and deployed by Intensive Partnerships for Effective Teaching allegedly harmed Teachers, Students and 6 others.

Risk domain
AI system safety, failures, and limitations Lack of capability or robustness
Occurred
Coverage
1 reportJun 2018

What happened

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.

Laws that address this harm

Policy angle: Classified under AI system safety, failures, and limitations (Lack of capability or robustness) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case in the United States.

Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).

News reports (1)

Titles link to the original publisher; report text is not reproduced here.

  1. Here's How Not to Improve Public Schools
    bloomberg.com · Cathy O'Neil

Who was involved

Alleged harmed party
Teachers Students Minority students Minority groups Epistemic integrity Educational communities Economically vulnerable students Economically vulnerable people

AI systems implicated

Intensive Partnerships for Effective Teaching teacher-evaluation systemAlgorithmic teacher evaluation programAlgorithmic management systemsAI-enabled decision support systems

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Unintentional
Timing
Post-deployment
Harm level
unclear
Sectors
education
Countries
US

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 7.3.

  • Reliability issues

    "Relying on general-purpose AI products that fail to fulfil their intended function can lead to harm. For example, general- purpose AI systems can make up facts (‘hallucination’), generate erroneous computer code, or pro...

    International AI Safety Report 2025 (Bengio2025)

  • Type 2: Bigger than expected

    Harm can result from AI that was not expected to have a large impact at all, such as a lab leak, a surprisingly addictive open-source product, or an unexpected repurposing of a research prototype.

    TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  • Type 3: Worse than expected

    AI intended to have a large societal impact can turn out harmful by mistake, such as a popular product that creates problems and partially solves them only for its users.

    TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  • Ethics and Morality Issues

    LMs need to pay more attention to universally accepted societal values at the level of ethics and morality, including the judgement of right and wrong, and its relationship with social norms and laws.

    Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

  • Safe learning

    "AGIs should avoid making fatal mistakes during the learning phase. Subproblems include safe exploration and distributional shift (DeepMind, OpenAI), and continual learning (Berkeley)."

    AGI Safety Literature Review (Everitt2018 )

  • Malign belief distributions

    "Christiano (2016) argues that the universal distribution M (Hutter, 2005; Solomonoff, 1964a,b, 1978) is malign. The argument is somewhat intricate, and is based on the idea that a hypothesis about the world often includ...

    AGI Safety Literature Review (Everitt2018 )

  • Meta-cognition

    "Agents that reason about their own computational resources and logically uncertain events can encounter strange paradoxes due to Godelian limitations (Fallenstein and Soares, 2015; Soares and Fallenstein, 2014, 2017) an...

    AGI Safety Literature Review (Everitt2018 )

  • Technical and operational risks

    "To date, technical limitations and vulnerabilities are present in most generative AI models in various contexts. Consequently, malicious users find it easier to breach an AI system’s safety and ethical guardrails to e...

    Regulating under Uncertainty: Governance Options for Generative AI (G'sell2024)

Incidents in the same risk subdomain

All incidents in this subdomain

Source record: incident #239 on the AI Incident Database · all 1 report