AI incident ·
University of Toronto's ProctorU Deployment Reportedly Disadvantaged BIPOC Students During Exam Check-Ins
On this page 8 sections
In brief
An AI system built by Proctoru and deployed by University Of Toronto allegedly harmed University Students, University Of Toronto Students and 3 others.
- Risk domain
- Discrimination and Toxicity
- Occurred
- Coverage
- 1 report
What happened
University of Toronto students reported that ProctorU and other online exam-monitoring services created discriminatory and stressful check-in experiences for BIPOC students during remote exams. One U of T student said ProctorU's identity-verification process often failed to recognize her passport, forcing manual checks and consuming exam check-in time. ProctorU said human proctors made final identity and integrity decisions.
Laws on record for this risk area
Policy angle: Classified under Discrimination and Toxicity (Unequal performance across groups) in the MIT AI Risk Repository taxonomy; 4 instruments on record cover this risk area in Canada.
- Quebec Law 25 (automated decisions and biometrics)
- Ontario Working for Workers Four Act (AI disclosure in job postings)
- India DPDP Act
- Law No. 132/2025 on artificial intelligence
Matched from the record's risk category and country to the instruments in effect recorded here: a law listed covers the area, which is not a finding that it applies to this incident. 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
- University Of Toronto
- Alleged developer
- Proctoru
- Alleged harmed party
- University Students, University Of Toronto Students, Students, Educational Communities, Students Of Color
Classification (MIT AI Risk Repository taxonomy)
- Risk domain
- Discrimination and Toxicity
- Risk subdomain
- 1.3 Unequal performance across groups
- Causal entity
- AI
- Intent
- Unintentional
- Timing
- Post-deployment
- Harm level
- none
- Sectors
- education
- Countries
- CA
Risk entries describing this failure mode
Entries from the MIT AI Risk Repository coded to subdomain 1.3.
- Bias and discrimination (value embedding)
"Generative AI models may also be subject to the “value embedding” phenomenon.361 “Value embedding” refers to the fact that developers of generative AI models strive to minimize biased outputs by retraining their models...
- Impact on affected communities
"It is important to include the perspectives or concerns of communities that are affected by model outcomes when designing and building models. Failing to include these perspectives makes it difficult to understand the r...
- Unfair capability distribution
"Performing worse for some groups than others in a way that harms the worse-off group"
- Fairness
Avoiding bias and ensuring no disparate performance
- Disparate Performance
The LLM’s performances can differ significantly across different groups of users. For example, the question-answering capability showed significant performance differences across different racial and social status groups...
- Ideological Homogenization from Value Embedding
"The increasing integration of general purpose AI models into every-day life raises concerns around their embedded normative values. The reach of a small number of AI models to a large number of people around the world c...
- Fairness
This challenge appears when the learning model leads to a decision that is biased to some sensitive attributes... data itself could be biased, which results in unfair decisions. Therefore, this problem should be solved o...
- Alienation
Alienation is the specific self-estrangement experienced at the time of technology use, typically surfaced through interaction with systems that under-perform for marginalized individuals
Incidents in the same risk subdomain
- Washington State DOL's AI Phone System Reportedly Failed to Provide Spanish-Language Service to Callers Requesting Spanish
- UK Facial Recognition System Reportedly Exhibits Higher False Positive Rates for Black and Asian Subjects
- Infinite Campus AI-Driven Student Risk Model Leads to Cuts in Support for Nevada's Low-Income Schools
- Police Use of Facial Recognition Software Causes Wrongful Arrests Without Defendant Knowledge
- Department for Work and Pensions (DWP) Algorithm Wrongly Flags 200,000 for Housing Benefit Fraud
- Facewatch Reported to Have Wrongfully Flagged Home Bargains Customer as Shoplifter
Source record: incident #140 on the AI Incident Database · all 1 report