Nigerian Software Engineer or American Data Scientist? GitHub Profile Recruitment Bias in Large Language Models
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929674659037184 |
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| author | Nakano, Takashi Shimari, Kazumasa Kula, Raula Gaikovina Treude, Christoph Cheong, Marc Matsumoto, Kenichi |
| author_facet | Nakano, Takashi Shimari, Kazumasa Kula, Raula Gaikovina Treude, Christoph Cheong, Marc Matsumoto, Kenichi |
| contents | Large Language Models (LLMs) have taken the world by storm, demonstrating their ability not only to automate tedious tasks, but also to show some degree of proficiency in completing software engineering tasks. A key concern with LLMs is their "black-box" nature, which obscures their internal workings and could lead to societal biases in their outputs. In the software engineering context, in this early results paper, we empirically explore how well LLMs can automate recruitment tasks for a geographically diverse software team. We use OpenAI's ChatGPT to conduct an initial set of experiments using GitHub User Profiles from four regions to recruit a six-person software development team, analyzing a total of 3,657 profiles over a five-year period (2019-2023). Results indicate that ChatGPT shows preference for some regions over others, even when swapping the location strings of two profiles (counterfactuals). Furthermore, ChatGPT was more likely to assign certain developer roles to users from a specific country, revealing an implicit bias. Overall, this study reveals insights into the inner workings of LLMs and has implications for mitigating such societal biases in these models. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_12544 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Nigerian Software Engineer or American Data Scientist? GitHub Profile Recruitment Bias in Large Language Models Nakano, Takashi Shimari, Kazumasa Kula, Raula Gaikovina Treude, Christoph Cheong, Marc Matsumoto, Kenichi Software Engineering Large Language Models (LLMs) have taken the world by storm, demonstrating their ability not only to automate tedious tasks, but also to show some degree of proficiency in completing software engineering tasks. A key concern with LLMs is their "black-box" nature, which obscures their internal workings and could lead to societal biases in their outputs. In the software engineering context, in this early results paper, we empirically explore how well LLMs can automate recruitment tasks for a geographically diverse software team. We use OpenAI's ChatGPT to conduct an initial set of experiments using GitHub User Profiles from four regions to recruit a six-person software development team, analyzing a total of 3,657 profiles over a five-year period (2019-2023). Results indicate that ChatGPT shows preference for some regions over others, even when swapping the location strings of two profiles (counterfactuals). Furthermore, ChatGPT was more likely to assign certain developer roles to users from a specific country, revealing an implicit bias. Overall, this study reveals insights into the inner workings of LLMs and has implications for mitigating such societal biases in these models. |
| title | Nigerian Software Engineer or American Data Scientist? GitHub Profile Recruitment Bias in Large Language Models |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2409.12544 |