@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T FAIRGAMER: Evaluating Social Biases in LLM-Based Video Game NPCs
%A Shi, Bingkang
%A Huang, Jen-tse
%A Long, Luo
%A Zong, Tianyu
%A Yi, Hongzhu
%A Wang, Yuanxiang
%A Hu, Songlin
%A Zhang, Xiaodan
%A Yao, Zhongjiang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F shi-etal-2026-fairgamer
%X 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.
%U https://aclanthology.org/2026.acl-long.2015/
%P 43530-43552
Markdown (Informal)
[FAIRGAMER: Evaluating Social Biases in LLM-Based Video Game NPCs](https://aclanthology.org/2026.acl-long.2015/) (Shi et al., ACL 2026)
ACL
- Bingkang Shi, Jen-tse Huang, Luo Long, Tianyu Zong, Hongzhu Yi, Yuanxiang Wang, Songlin Hu, Xiaodan Zhang, and Zhongjiang Yao. 2026. FAIRGAMER: Evaluating Social Biases in LLM-Based Video Game NPCs. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 43530–43552, San Diego, California, United States. Association for Computational Linguistics.