AI incident ·
Canadian Universities' AI Proctoring Tools Reportedly Collected Student Biometric Data Without Adequate Consent
In brief
An AI system built by Respondus Monitor, Proctoru and 4 others and deployed by Universities, Professors and 2 others allegedly harmed University Students, Universities and 5 others.
- Risk domain
- Discrimination and Toxicity
- Occurred
- Coverage
- 4 reports
What happened
During the COVID-19 pandemic, Canadian universities reportedly used AI-enabled remote-proctoring tools for online exams. A University of Ottawa project supported by the Office of the Privacy Commissioner of Canada found that tools such as Respondus Monitor, ProctorU, ProctorTrack, Proctorio, ProctorExam, and Examity collected student personal or biometric data under conditions not conducive to meaningful consent.
Laws that address this harm
Policy angle: Classified under Discrimination and Toxicity (Unfair discrimination and misrepresentation) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.
- Colorado AI Act
- India DPDP Act
- Law No. 132/2025 on artificial intelligence
- NYC Local Law 144 (automated employment decision tools)
- EU AI Act
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 (4)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
- Alleged deployer
- Universities, Professors, Educators, Canadian Universities
- Alleged harmed party
- University Students, Universities, Students, Educational Communities, Canadian Students, Privacy, Biometric Data Subjects
Classification (MIT AI Risk Repository taxonomy)
- Risk domain
- Discrimination and Toxicity
- Risk subdomain
- 1.1 Unfair discrimination and misrepresentation
- 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 1.1.
- Discrimination
"Discrimination - Unfair or inadequate treatment or arbitrary distinction based on a person’s race, ethnicity, age, gender, sexual preference, religion, national origin, marital status, disability, language, or other pro...
- Harms of Representation and Other Biases
"A pretrained LLM generally has many of the stereotypical biases commonly present in the human society (Touvron et al., 2023). This makes it difficult for users to trust that LLMs will work well for them and not produce...
- Risks from bias and underrepresentation
"The outputs and impacts of general- purpose AI systems can be biased with respect to various aspects of human identity, including race, gender, culture, age, and disability. This creates risks in high- stakes domains su...
- Bias
"General-purpose AI systems can amplify social and political biases, causing concrete harm. They frequently display biases with respect to race, gender, culture, age, disability, political opinion, or other aspects of hu...
- Biased Training Data
"Compared with the definition of toxicity, the definition of bias is more subjective and contextdependent. Based on previous work [97], [101], we describe the bias as disparities that could raise demographic differences...
- Toxicity and Bias Tendencies
"Extensive data collection in LLMs brings toxic content and stereotypical bias into the training data."
- Bias
"The training datasets of LLMs may contain biased information that leads LLMs to generate outputs with social biases"
- Broken systems
"These are the most mentioned cases. They refer to situations where the algorithm or the training data lead to unreliable outputs. These systems frequently assign disproportionate weight to some variables, like race or g...
Incidents in the same risk subdomain
- DOGE Reportedly Relied on Unvetted ChatGPT Outputs in Canceling National Endowment for the Humanities Grants
- Sora Video Generator Has Reportedly Been Creating Biased Human Representations Across Race, Gender, and Disability
- Meta AI Characters Allegedly Exhibited Racism, Fabricated Identities, and Exploited User Trust
- Algorithmic Bias in French Welfare System Allegedly Discriminates Against Marginalized Groups
- Alleged AI-Generated Photo Alteration Leads to Inappropriate Modifications in Speaker's Conference Picture
- Department for Work and Pensions (DWP) AI Systems Allegedly Discriminate Against Single Mothers
Source record: incident #424 on the AI Incident Database · all 4 reports