AI incident #302 ·
Dartmouth's Geisel School of Medicine Reportedly Used Canvas Activity Logs to Accuse Students of Cheating
What happened
Dartmouth's Geisel School of Medicine reportedly used Canvas learning-management activity logs and an internal analysis process to investigate remote-exam cheating during the 2020–2021 academic year. Seventeen medical students were charged after the school inferred that they accessed course materials during exams, but students and outside technical reviewers said automated Canvas activity may have been misread as intentional misconduct. Dartmouth later dropped the charges.
Editor's notes (AI Incident Database)
This record is retained as an incident because it describes a specific academic-misconduct investigation in which Geisel School of Medicine reportedly used Canvas learning-management activity logs and an internal analysis process to accuse students of cheating. The record is AIID-borderline because the implicated system appears to be a semi-opaque activity-log analysis rather than a conventional AI model; retention depends on treating the log-analysis process as an algorithmic decision-support system used in a high-stakes disciplinary context.
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 (1)
Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.
Who was involved
- Alleged deployer
- Geisel School of Medicine
- Alleged developer
- Geisel School of Medicine's Technology staff Canvas
- Alleged harmed party
- University students Students Sirey Zhang Medical students Geisel School of Medicine's students Geisel School of Medicine's professors Geisel School of Medicine's accused students Epistemic integrity Educational communities
AI systems implicated
Learning management system activity logsCanvasAutomated proctoring 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
- Unintentional
- Timing
- Post-deployment
- Harm level
- —
- Sectors
- —
- 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...
- 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...
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