LLM Nepotism in Organizational Governance
Large language models are increasingly used to support organizational decisions from hiring to governance, raising fairness concerns in AI-assisted evaluation. Prior work has focused mainly on demographic bias and broader preference effects, rather than on whether evaluators reward expressed trust in AI itself. We study this phenomenon as LLM Nepotism, an attitude-driven bias channel in which favorable signals toward AI are rewarded even when they are not relevant to role-related merit. We introduce a two-phase simulation pipeline that first isolates AI-trust preference in qualification-matched resume screening and then examines its downstream effects in board-level decision making. Acros...
| Authors | Shunqi Mao, Wei Guo, Dingxin Zhang et al. |
|---|---|
| Primary category | cs.CY |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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