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Major universities are using EAB's Navigate software to assign 'risk' scores to students, incorporating race as a 'high-impact predictor.' Investigations revealed that this practice results in Black and Latinx students being disproportionately labeled as 'high risk,' which influences academic advising and potentially steers these students away from certain majors, raising concerns about algorithmic bias and educational equity.
Several major universities are using a tool that uses race as one factor to predict student success.
Domain classification, causal taxonomy, severity scores, and national security assessments were LLM-classified and may contain errors.
Unequal treatment of individuals or groups by AI, often based on race, gender, or other sensitive characteristics, resulting in unfair outcomes and unfair representation of those groups.
Also classified as
Overworked academic advisors rely on the proprietary risk scores to make critical educational recommendations without understanding how the scores are calculated.
The risk scoring algorithm is proprietary and protected, leaving university administrators and advisors unable to explain or verify the decision-making process.
The EAB Navigate system explicitly uses race as a predictor, resulting in Black and Latinx students being disproportionately labeled as high risk and unfairly steered away from competitive majors.
AI system
Due to a decision or action made by an AI system
Unintentional
Due to an unexpected outcome from pursuing a goal
Post-deployment
Occurring after the AI model has been trained and deployed
The discriminatory risk scores and subsequent steering of minority students are caused by the outputs of the deployed EAB Navigate AI system, which was an unintended consequence of trying to improve student retention.
Added categories peak at 2: Minor (Psychological Harm, Inferred)
The incident involves the widespread use of EAB's Navigate software in US universities, which uses race as a predictor to assign risk scores, disproportionately labeling minority students as 'high risk'. While raising significant concerns regarding algorithmic bias, civil rights, and educational equity, the national security implications are minor and manageable through standard civil rights enforcement and educational policy.
The Navigate software acts as an advising assistant, providing risk scores and major recommendations to help advisors make decisions, rather than taking autonomous actions itself.
High Risk: The system is used in education ('Educational and vocational training systems affecting access to education') to evaluate students and predict success, which directly affects their academic trajectory and access to opportunities.
The system models student academic behavior and historical data to predict the risk of dropping out, which is then used by universities to allocate advising resources.
The report provides detailed, data-backed evidence from public records requests and interviews with university administrators, students, and experts. The role of the AI system, the specific variables used (race), and the resulting disparities are clearly documented across multiple institutions.
EAB claims the tool is intended to highlight existing disparities to prompt institutional support rather than to steer students, though the practical effect reported is steering.
Technology
Proprietary Black-Box Algorithms
Proprietary nature prevents universities from auditing bias in risk scores.
Historical Bias Encoding
Models trained on old data encode past discriminatory practices.
Data Inputs
Race as a Risk Predictor
Using race directly as a predictor leads to racially disparate risk scores.
Omission of Financial Data
Failing to include financial struggles, a primary reason for dropping out.
Biased Historical Student Data
Using 2 to 10 years of historic outcomes data encodes systemic disparities.
Human Factors
Adviser Bias and Steering
Advisers steer minority students to 'easier' majors based on risk scores.
Overreliance on Quick Snapshots
Overworked advisers rely on 30-second dashboards instead of deep review.
Lack of Student Awareness
Students are judged by algorithms without their knowledge or consent.
Process and Methods
Inadequate Adviser Training
Universities provide little guidance on how to interpret or use risk scores.
High Student-to-Adviser Ratios
Ratios up to 700-to-1 force reliance on automated risk scores.
Inappropriate Use of Risk Explorer
Software features encourage pushing students out of preferred majors.
Management
Prioritizing Retention over Equity
Schools adopt software to preserve tuition revenue amidst funding cuts.
Lack of Algorithmic Due Diligence
Administrators buy software without understanding its underlying variables.
Underinvestment in Student Support
Buying software instead of funding actual tutors, advisers, or micro-loans.
Regulatory Environment
Lack of Algorithmic Auditing
No regulations require independent bias testing for education software.
Proprietary IP Protections
Trade secret protections shield the algorithms from public scrutiny.
AI system involved
Identified by automated extraction; can differ from the alleged developers above.
