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AI incident #9 ·

New York City Reportedly Released Disputed Value-Added Teacher Ratings for Thousands of Teachers

Open on the AI Incident Database 6 news reports Synced from the AIID API · record last edited 5 Sep 2026

What happened

After a legal dispute over public-records requests, the New York City Department of Education released Teacher Data Reports that used a value-added statistical model to rate thousands of public-school teachers based on estimated student test-score growth. News outlets published the ratings in February 2012, and teachers, unions, and analysts disputed the scores as imprecise and potentially misleading, citing large error margins, limited samples, and apparent roster-linkage errors.

Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.

News reports (6)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

  1. A NYC Math Teacher Fights Back After Receiving an Unfair 'Unsatisfactory' Rating from a Principal
    parentadvocates.org · Parent Advocates · AIID #1332
  2. New York City Teacher Ratings: Teacher Data Reports Publicly Released Amid Controversy
    huffingtonpost.com · Emmeline Zhao · AIID #1333
  3. Reign Of Error: The Publication Of Teacher Data Reports In New York City
    shankerinstitute.org · Matthew Di Carlo · AIID #1334
  4. Teachers plan widespread appeals of 'unfair' evaluations
    politico.com · Jessica Bakeman · AIID #1335
  5. Master teacher suing New York state over ‘ineffective’ rating is going to court
    washingtonpost.com · Valerie Strauss · AIID #1330
  6. New data show more than half of NYC teachers judged, in part, by test scores they don’t directly affect
    cityandstateny.com · Matt Barnum · AIID #1331

Who was involved

Alleged harmed party
Teachers Students New York City teachers New York City students Epistemic integrity Educational communities
On AIID: Teachers, Students, New York City teachers, New York City students, Epistemic integrity, Educational communities

AI systems implicated

New York City value-added teacher-evaluation modelNew York City teacher data reportsAlgorithmic management systemsAI-enabled decision support systems

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Intentional
Timing
Post-deployment
Harm level
none
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 )

  • Misaligned consequentialist reasoning

    "As we think about even more intelligent and advanced AI assistants, perhaps outperforming humans on many cognitive tasks, the question of how humans can successfully control such an assistant looms large. To achieve the...

    The Ethics of Advanced AI Assistants (Gabriel2024)

Linked by AIID editors or by its text-similarity model.

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