Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiring
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910782150672384 |
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| author | Korkmaz, Buse Sibel Nair, Rahul Daly, Elizabeth M. Anagnostopoulos, Evangelos Varytimidis, Christos Chanona, Antonio del Rio |
| author_facet | Korkmaz, Buse Sibel Nair, Rahul Daly, Elizabeth M. Anagnostopoulos, Evangelos Varytimidis, Christos Chanona, Antonio del Rio |
| contents | Foundation models require fine-tuning to ensure their generative outputs align with intended results for specific tasks. Automating this fine-tuning process is challenging, as it typically needs human feedback that can be expensive to acquire. We present AutoRefine, a method that leverages reinforcement learning for targeted fine-tuning, utilizing direct feedback from measurable performance improvements in specific downstream tasks. We demonstrate the method for a problem arising in algorithmic hiring platforms where linguistic biases influence a recommendation system. In this setting, a generative model seeks to rewrite given job specifications to receive more diverse candidate matches from a recommendation engine which matches jobs to candidates. Our model detects and regulates biases in job descriptions to meet diversity and fairness criteria. The experiments on a public hiring dataset and a real-world hiring platform showcase how large language models can assist in identifying and mitigation biases in the real world. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07324 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiring Korkmaz, Buse Sibel Nair, Rahul Daly, Elizabeth M. Anagnostopoulos, Evangelos Varytimidis, Christos Chanona, Antonio del Rio Machine Learning Foundation models require fine-tuning to ensure their generative outputs align with intended results for specific tasks. Automating this fine-tuning process is challenging, as it typically needs human feedback that can be expensive to acquire. We present AutoRefine, a method that leverages reinforcement learning for targeted fine-tuning, utilizing direct feedback from measurable performance improvements in specific downstream tasks. We demonstrate the method for a problem arising in algorithmic hiring platforms where linguistic biases influence a recommendation system. In this setting, a generative model seeks to rewrite given job specifications to receive more diverse candidate matches from a recommendation engine which matches jobs to candidates. Our model detects and regulates biases in job descriptions to meet diversity and fairness criteria. The experiments on a public hiring dataset and a real-world hiring platform showcase how large language models can assist in identifying and mitigation biases in the real world. |
| title | Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiring |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2501.07324 |