AIPolicyTracker
Menu

AI incident ·

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

2 news reports Synced from source · record last edited 3 Sep 2026

In brief

An AI system built by University of Texas at Austin researchers and deployed by University of Texas at Austin's Department of Computer Science, Risto Miikkulainen and 1 other allegedly harmed University students, University of Texas at Austin PhD applicants of marginalized groups and 6 others.

Risk domain
Discrimination and Toxicity Unfair discrimination and misrepresentation
Occurred
Coverage
2 reportsDec 2020

What happened

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.

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 in the United States.

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 (2)

Titles link to the original publisher; report text is not reproduced here.

  1. Uni revealed it killed off its PhD-applicant screening AI – just as its inventors gave a lecture about the tech
    theregister.com · Katyanna Quach
  2. The Death and Life of an Admissions Algorithm
    insidehighered.com · Lilah Burke

Who was involved

Alleged harmed party
University students University of Texas at Austin PhD applicants of marginalized groups University applicants Students PhD applicants from underrepresented groups PhD applicants Educational communities Computer science PhD applicants

AI systems implicated

GRaduate ADmissions Evaluator (GRADE)Automated admissions screening systemsAI-enabled decision support systems

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Unintentional
Timing
Post-deployment
Harm level
none
Sectors
education
Countries
US

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...

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • 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...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  • 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...

    International Scientific Report on the Safety of Advanced AI (Bengio2024)

  • 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...

    International AI Safety Report 2025 (Bengio2025)

  • 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...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Toxicity and Bias Tendencies

    "Extensive data collection in LLMs brings toxic content and stereotypical bias into the training data."

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Bias

    "The training datasets of LLMs may contain biased information that leads LLMs to generate outputs with social biases"

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • 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...

    Navigating the Landscape of AI Ethics and Responsibility (Cunha2023)

Linked by editors or by text similarity in the source dataset.

Incidents in the same risk subdomain

All incidents in this subdomain

Source record: incident #135 on the AI Incident Database · all 2 reports