Description: Several major universities are using a tool that uses race as one factor to predict student success.
Entities
View all entitiesAlleged: EAB developed an AI system deployed by University of Massachusetts Amherst , University of Wisconsin–Milwaukee , University of Houston , Texas A&M University , Georgia State University and more than 500 colleges, which harmed Black college students , Latinx college students and indigenous students.
CSETv1 Taxonomy Classifications
Taxonomy DetailsIncident Number
The number of the incident in the AI Incident Database.
99
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
1.1. Unfair discrimination and misrepresentation
Risk Domain
The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
- Discrimination and Toxicity
Entity
Which, if any, entity is presented as the main cause of the risk
AI
Timing
The stage in the AI lifecycle at which the risk is presented as occurring
Post-deployment
Intent
Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
Unintentional
Incident Reports
Reports Timeline
Major universities are using their students’ race, among other variables, to predict how likely they are to drop out of school. Documents obtained by The Markup through public records requests show that some schools are using education rese…
Variants
A "variant" is an AI incident similar to a known case—it has the same causes, harms, and AI system. Instead of listing it separately, we group it under the first reported incident. Unlike other incidents, variants do not need to have been reported outside the AIID. Learn more from the research paper.
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