OOF-AII-046318

UT Austin's GRADE Algorithm Reportedly Reduced Review of Lower-Scored PhD Applicants Amid Bias Concerns

From the 2013 through 2019 admissions cycles, UT Austin's Department of Computer Science used GRADE, a statistical machine-learning system trained on past admissions decisions, to score and organize PhD applications. Critics said the system could reproduce historical admissions inequities and reduce attention to lower-scored applicants, while UT Austin said human reviewers still evaluated each file and later discontinued the tool.

Event date: 2012-12-01
Country: United States of America
Severity: Moderate
Evidence state: Single-source

Assessment status

Generated from available public incident evidence using OOF® governance methodology. It is not an official investigation, legal determination, regulatory finding, certification, or OOF® Approved Assessment.

View source record from AI Incident Database (Responsible AI Collaborative)

Assessment confidence dimensions

Source corroboration: Single-source
Extraction completeness: High
Methodological assignment confidence: Limited

Known facts from the incident record

The incident record identifies the event date as 2012-12-01.
The incident record identifies the location as United States of America.

Reported claims

From the 2013 through 2019 admissions cycles, UT Austin's Department of Computer Science used GRADE, a statistical machine-learning system trained on past admissions decisions, to score and organize PhD applications. Critics said the system could reproduce historical admissions inequities and reduce attention to lower-scored applicants, while UT Austin said human reviewers still evaluated each file and later discontinued the tool.

Material unknowns

Independent corroboration is not established in the available incident record.

Primary governance problem

The incident appears to expose a governance problem involving decision, learning. Available reporting supports preliminary methodological interpretation, not a finding of fault or non-compliance.

Architecture intelligence

CLIA® — Cognitive Governance Intelligence Architecture

Primary architecture

The reported decision, learning concern falls within the governed questions represented by CLIA® — Cognitive Governance Intelligence Architecture.

Governance focus: How should decision, learning be governed in the conditions described by the available incident evidence?

Deeper methodology is withheld because the available evidence does not justify a Parent Standard or Core Module.

PGA™ — Prediction Governance Architecture

Contributing architecture

The reported support concern falls within the governed questions represented by PGA™ — Prediction Governance Architecture.

Governance focus: How should support be governed in the conditions described by the available incident evidence?

Deeper methodology is withheld because the available evidence does not justify a Parent Standard or Core Module.

Preliminary governance questions

How should decision, learning be governed in the conditions described by the available incident evidence?
How should support be governed in the conditions described by the available incident evidence?
What operational authority and controls applied to University Of Texas At Austin'S Department Of Computer Science, Risto Miikkulainen, Austin Waters at the relevant time?
What evidence would establish how Graduate Admissions Evaluator (Grade), Automated Admissions Screening Systems, Ai Enabled Decision Support Systems produced or enabled the reported outcome?
What additional independent evidence is required to resolve the material evidence gaps?

Evidence gaps and limitations

Only one independent source group is represented; corroboration is not established.
AI technology is not specified in the source record.

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