AI incident ·
New York City Reportedly Released Disputed Value-Added Teacher Ratings for Thousands of Teachers
In brief
An AI system built and deployed by New York City Department of Education allegedly harmed Teachers, Students and 4 others.
- Risk domain
- AI system safety, failures, and limitations
- Occurred
- Coverage
- 6 reports
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.
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.
- Texas Responsible AI Governance Act (TRAIGA)
- California SB 53
- Tennessee ELVIS Act
- EO 14179
- New York RAISE Act (frontier model safety)
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 (6)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
- Alleged deployer
- New York City Department of Education
- Alleged developer
- New York City Department of Education
- Alleged harmed party
- 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)
- Risk domain
- AI system safety, failures, and limitations
- Risk subdomain
- 7.3 Lack of capability or robustness
- 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...
- 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.
- 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.
- 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.
- 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)."
- 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...
- 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...
- 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...
Related incidents
Linked by editors or by text similarity in the source dataset.
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Source record: incident #9 on the AI Incident Database · all 6 reports