AI incident ·
International Baccalaureate’s 2020 Calculated-Grades Model Reportedly Lowered Students' Scores and College Prospects
In brief
An AI system built and deployed by International Baccalaureate Organization allegedly harmed International Baccalaureate Students, Students and 5 others.
- Risk domain
- AI system safety, failures, and limitations
- Occurred
- Coverage
- 1 report
What happened
After Covid-19 canceled May 2020 exams, the Geneva-based International Baccalaureate reportedly used a calculated-grades model based on coursework, predicted grades, and historical school results to estimate final marks. Students, parents, and teachers reportedly said the opaque model produced unexpectedly low scores, threatening university admissions, scholarships, and study plans across multiple countries.
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.
- India DPDP Act
- Law on Artificial Intelligence (2025)
- Law No. 132/2025 on artificial intelligence
- EU AI Act
- Texas Responsible AI Governance Act (TRAIGA)
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.
Who was involved
- Alleged deployer
- International Baccalaureate Organization
- Alleged developer
- International Baccalaureate Organization
- Alleged harmed party
- International Baccalaureate Students, Students, Minors, Teachers, Parents, Educational Communities, University Applicants
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
- Unintentional
- Timing
- Post-deployment
- Harm level
- none
- Sectors
- education
- Countries
- —
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...
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Source record: incident #78 on the AI Incident Database · all 1 report