AI incident #849 ·
AI Detection Tools Allegedly Misidentify Neurodivergent and ESL Students' Work as AI-Generated in Academic Settings
What happened
AI writing detection tools have reportedly continued to falsely flag genuine student work as AI-generated, disproportionately impacting ESL and neurodivergent students. Specific cases include Moira Olmsted, Ken Sahib, and Marley Stevens, who were penalized despite writing their work independently. Such tools reportedly exhibit biases, leading to academic penalties, probation, and strained teacher-student relationships.
Editor's notes (AI Incident Database)
Reconstructing the timeline of events: (1) Sometime in 2023: Central Methodist University is reported to have used Turnitin to analyze assignments for AI usage. Moira Olmsted’s writing is flagged as AI-generated, leading to her receiving a zero and a warning. (2) Sometime in 2023: Ken Sahib, an ESL student at Berkeley College, is reported to have been penalized after AI detection tools flagged his assignment as AI-generated. (3) Sometime in late 2023 or early 2024: Marley Stevens is reported to have been placed on academic probation after Turnitin falsely identifies her work as AI-generated, though she purports to have only used Grammarly for minor edits. (4) October 18, 2024: Bloomberg publishes findings that leading AI detectors falsely flag 1%-2% of essays as AI-generated, with higher error rates for ESL students. (This date is set as the incident date for convenience.)
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
- Universities Colleges Central Methodist University Berkeley College
- Alleged harmed party
- Students Neurodivergent students Moira Olmsted Marley Stevens Ken Sahib ESL students Epistemic integrity Educational communities
AI systems implicated
TurnitinGPTZeroCopyleaksAI-enabled decision support systemsAI detector technology
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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