Case · 2024
UW academic study (Mistral AI, Salesforce, Contextual AI open-source LLMs)
Academic audit of 3 open-source LLMs for resume-screening bias (Wilson & Caliskan, AAAI/ACM 2024); tested on 550+ real resumes across 9 occupations
Maturity stage
Research
Use-case type
Analysis
Function
Human Resources/Talent
Company size
N/A (research)
Evidence
White-associated names preferred 85% vs Black-associated names 9%; demonstrates that off-the-shelf LLMs carry resume-screening bias
ROI / outcome figure
Research finding; no deployment
What RAPID would have flagged
Failure mode: Data — Poor data quality, bias in training data, or insufficient data volume leading to unreliable AI outputs
Dimensions a pre-deployment RAPID assessment would have surfaced
- Data & Technical Readiness (low score < 50%)
Mitigations the framework recommends
- Conduct data quality audit before model training or deployment
- Implement bias detection and monitoring for proxy variables
- Establish data governance with clear ownership and quality standards
- Build data validation pipelines with automated quality checks
Dimensions this case illuminates
RData & Technical Readiness
Is the data infrastructure and technical capability adequate?
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