Summary of Jobfair: a Framework For Benchmarking Gender Hiring Bias in Large Language Models, by Ze Wang et al.
JobFair: A Framework for Benchmarking Gender Hiring Bias in Large Language Models
by Ze Wang, Zekun Wu, Xin Guan, Michael Thaler, Adriano Koshiyama, Skylar Lu, Sachin Beepath, Ediz Ertekin Jr., Maria Perez-Ortiz
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework benchmarks hierarchical gender hiring bias in Large Language Models (LLMs) for resume scoring, revealing significant issues of reverse gender hiring bias and overdebiasing. The contributions are fourfold: introducing a new construct grounded in labour economics, legal principles, and critiques of current bias benchmarks; developing rigorous statistical and computational hiring bias metrics; analyzing gender hiring biases in ten state-of-the-art LLMs; and providing a user-friendly demo and resume dataset to support adoption. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows that seven out of ten LLMs have significant biases against males in at least one industry. The healthcare industry is the most biased against males, and the bias remains invariant with resume content for eight out of ten LLMs. This framework can be generalized to other social traits and tasks. |
