Revealing Hidden Bias in AI: Lessons from Large Language Models
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910660656365568 |
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| author | Beatty, Django Masanthia, Kritsada Kaphol, Teepakorn Sethi, Niphan |
| author_facet | Beatty, Django Masanthia, Kritsada Kaphol, Teepakorn Sethi, Niphan |
| contents | As large language models (LLMs) become integral to recruitment processes, concerns about AI-induced bias have intensified. This study examines biases in candidate interview reports generated by Claude 3.5 Sonnet, GPT-4o, Gemini 1.5, and Llama 3.1 405B, focusing on characteristics such as gender, race, and age. We evaluate the effectiveness of LLM-based anonymization in reducing these biases. Findings indicate that while anonymization reduces certain biases, particularly gender bias, the degree of effectiveness varies across models and bias types. Notably, Llama 3.1 405B exhibited the lowest overall bias. Moreover, our methodology of comparing anonymized and non-anonymized data reveals a novel approach to assessing inherent biases in LLMs beyond recruitment applications. This study underscores the importance of careful LLM selection and suggests best practices for minimizing bias in AI applications, promoting fairness and inclusivity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16927 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Revealing Hidden Bias in AI: Lessons from Large Language Models Beatty, Django Masanthia, Kritsada Kaphol, Teepakorn Sethi, Niphan Artificial Intelligence Computers and Society I.2.7; K.4.1 As large language models (LLMs) become integral to recruitment processes, concerns about AI-induced bias have intensified. This study examines biases in candidate interview reports generated by Claude 3.5 Sonnet, GPT-4o, Gemini 1.5, and Llama 3.1 405B, focusing on characteristics such as gender, race, and age. We evaluate the effectiveness of LLM-based anonymization in reducing these biases. Findings indicate that while anonymization reduces certain biases, particularly gender bias, the degree of effectiveness varies across models and bias types. Notably, Llama 3.1 405B exhibited the lowest overall bias. Moreover, our methodology of comparing anonymized and non-anonymized data reveals a novel approach to assessing inherent biases in LLMs beyond recruitment applications. This study underscores the importance of careful LLM selection and suggests best practices for minimizing bias in AI applications, promoting fairness and inclusivity. |
| title | Revealing Hidden Bias in AI: Lessons from Large Language Models |
| topic | Artificial Intelligence Computers and Society I.2.7; K.4.1 |
| url | https://arxiv.org/abs/2410.16927 |