FAIRGAMER: Evaluating Social Biases in LLM-Based Video Game NPCs
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915741899423744 |
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| author | Shi, Bingkang Huang, Jen-tse Luo, Long Zong, Tianyu Yi, Hongzhu Wang, Yuanxiang Hu, Songlin Zhang, Xiaodan Yao, Zhongjiang |
| author_facet | Shi, Bingkang Huang, Jen-tse Luo, Long Zong, Tianyu Yi, Hongzhu Wang, Yuanxiang Hu, Songlin Zhang, Xiaodan Yao, Zhongjiang |
| contents | Large Language Models (LLMs) have increasingly enhanced or replaced traditional Non-Player Characters (NPCs) in video games. However, these LLM-based NPCs inherit underlying social biases (e.g., race or class), posing fairness risks during in-game interactions. To address the limited exploration of this issue, we introduce FairGamer, the first benchmark to evaluate social biases across three interaction patterns: transaction, cooperation, and competition. FairGamer assesses four bias types, including class, race, age, and nationality, across 12 distinct evaluation tasks using a novel metric, FairMCV. Our evaluation of seven frontier LLMs reveals that: (1) models exhibit biased decision-making, with Grok-4-Fast demonstrating the highest bias (average FairMCV = 76.9%); and (2) larger LLMs display more severe social biases, suggesting that increased model capacity inadvertently amplifies these biases. We release FairGamer at https://github.com/Anonymous999-xxx/FairGamer to facilitate future research on NPC fairness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17825 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FAIRGAMER: Evaluating Social Biases in LLM-Based Video Game NPCs Shi, Bingkang Huang, Jen-tse Luo, Long Zong, Tianyu Yi, Hongzhu Wang, Yuanxiang Hu, Songlin Zhang, Xiaodan Yao, Zhongjiang Artificial Intelligence Large Language Models (LLMs) have increasingly enhanced or replaced traditional Non-Player Characters (NPCs) in video games. However, these LLM-based NPCs inherit underlying social biases (e.g., race or class), posing fairness risks during in-game interactions. To address the limited exploration of this issue, we introduce FairGamer, the first benchmark to evaluate social biases across three interaction patterns: transaction, cooperation, and competition. FairGamer assesses four bias types, including class, race, age, and nationality, across 12 distinct evaluation tasks using a novel metric, FairMCV. Our evaluation of seven frontier LLMs reveals that: (1) models exhibit biased decision-making, with Grok-4-Fast demonstrating the highest bias (average FairMCV = 76.9%); and (2) larger LLMs display more severe social biases, suggesting that increased model capacity inadvertently amplifies these biases. We release FairGamer at https://github.com/Anonymous999-xxx/FairGamer to facilitate future research on NPC fairness. |
| title | FAIRGAMER: Evaluating Social Biases in LLM-Based Video Game NPCs |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2508.17825 |