首个评估游戏AI角色社会偏见的基准，揭示大模型越强偏见越明显。
FAIRGAMER: Evaluating Social Biases in LLM-Based Video Game NPCs
- 构建三类交互场景下的首个游戏NPC偏见评估基准。
- 7个前沿大模型中Grok-4-Fast偏见最严重（平均76.9%）。
- 模型越大偏见越深，适合关注AI公平性的研究者参考。
大型语言模型（LLMs）正越来越多地用于增强或替代视频游戏中的非玩家角色（NPC）。然而，这些基于LLM的NPC会继承潜在的社会偏见（如种族或阶级），在游戏互动中带来公平性风险。为填补这一研究空白，我们提出FairGamer，首个针对三种交互模式（交易、合作、竞争）评估社会偏见的基准。FairGamer涵盖四类偏见（阶级、种族、年龄、国籍），通过12项任务并采用新指标FairMCV进行评估。对7个前沿大模型的评测显示：（1）模型存在明显偏见决策，其中Grok-4-Fast表现最差（平均FairMCV=76.9%）；（2）更大的模型表现出更严重的社会偏见，表明模型容量提升反而加剧了偏见问题。相关代码与数据已开源，网址为https://github.com/Anonymous999-xxx/FairGamer。
原文摘要 · Abstract (English)
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.
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