Once Upon a Team: Investigating Bias in LLM-Driven Software Team Composition and Task Allocation
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866909983449284608 |
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| author | Parziale, Alessandra Voria, Gianmario Pontillo, Valeria Di Salle, Amleto Pelliccione, Patrizio Catolino, Gemma Palomba, Fabio |
| author_facet | Parziale, Alessandra Voria, Gianmario Pontillo, Valeria Di Salle, Amleto Pelliccione, Patrizio Catolino, Gemma Palomba, Fabio |
| contents | LLMs are increasingly used to boost productivity and support software engineering tasks. However, when applied to socially sensitive decisions such as team composition and task allocation, they raise concerns of fairness. Prior studies have revealed that LLMs may reproduce stereotypes; however, these analyses remain exploratory and examine sensitive attributes in isolation. This study investigates whether LLMs exhibit bias in team composition and task assignment by analyzing the combined effects of candidates' country and pronouns. Using three LLMs and 3,000 simulated decisions, we find systematic disparities: demographic attributes significantly shaped both selection likelihood and task allocation, even when accounting for expertise-related factors. Task distributions further reflected stereotypes, with technical and leadership roles unevenly assigned across groups. Our findings indicate that LLMs exacerbate demographic inequities in software engineering contexts, underscoring the need for fairness-aware assessment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03857 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Once Upon a Team: Investigating Bias in LLM-Driven Software Team Composition and Task Allocation Parziale, Alessandra Voria, Gianmario Pontillo, Valeria Di Salle, Amleto Pelliccione, Patrizio Catolino, Gemma Palomba, Fabio Software Engineering LLMs are increasingly used to boost productivity and support software engineering tasks. However, when applied to socially sensitive decisions such as team composition and task allocation, they raise concerns of fairness. Prior studies have revealed that LLMs may reproduce stereotypes; however, these analyses remain exploratory and examine sensitive attributes in isolation. This study investigates whether LLMs exhibit bias in team composition and task assignment by analyzing the combined effects of candidates' country and pronouns. Using three LLMs and 3,000 simulated decisions, we find systematic disparities: demographic attributes significantly shaped both selection likelihood and task allocation, even when accounting for expertise-related factors. Task distributions further reflected stereotypes, with technical and leadership roles unevenly assigned across groups. Our findings indicate that LLMs exacerbate demographic inequities in software engineering contexts, underscoring the need for fairness-aware assessment. |
| title | Once Upon a Team: Investigating Bias in LLM-Driven Software Team Composition and Task Allocation |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2601.03857 |