Ethical Considerations of Large Language Models in Game Playing
It addresses ethical problems for AI developers and users in gaming, but is incremental as it focuses on a specific case without broad new solutions.
This paper investigates ethical issues in large language models (LLMs) applied to game playing, using Werewolf as a case study, and finds that LLMs exhibit gender bias affecting fairness and player experience, with roles like Guard and Werewolf showing higher sensitivity to gender information.
Large language models (LLMs) have demonstrated tremendous potential in game playing, while little attention has been paid to their ethical implications in those contexts. This work investigates and analyses the ethical considerations of applying LLMs in game playing, using Werewolf, also known as Mafia, as a case study. Gender bias, which affects game fairness and player experience, has been observed from the behaviour of LLMs. Some roles, such as the Guard and Werewolf, are more sensitive than others to gender information, presented as a higher degree of behavioural change. We further examine scenarios in which gender information is implicitly conveyed through names, revealing that LLMs still exhibit discriminatory tendencies even in the absence of explicit gender labels. This research showcases the importance of developing fair and ethical LLMs. Beyond our research findings, we discuss the challenges and opportunities that lie ahead in this field, emphasising the need for diving deeper into the ethical implications of LLMs in gaming and other interactive domains.