@inproceedings{shi-etal-2026-fairgamer,
    title = "{FAIRGAMER}: Evaluating Social Biases in {LLM}-Based Video Game {NPC}s",
    author = "Shi, Bingkang  and
      Huang, Jen-tse  and
      Long, Luo  and
      Zong, Tianyu  and
      Yi, Hongzhu  and
      Wang, Yuanxiang  and
      Hu, Songlin  and
      Zhang, Xiaodan  and
      Yao, Zhongjiang",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.2015/",
    pages = "43530--43552",
    ISBN = "979-8-89176-390-6",
    abstract = "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/BingkangShi/FairGamer to facilitate future research on NPC fairness."
}