Utilitarian games such as dictator games to measure fairness have been studied in the social sciences for decades. These games have given us insight into not only how humans view fairness but also in what conditions the frequency of fairness, altruism and greed increase or decrease. While these games have traditionally been focused on humans, the rise of AI gives us the ability to study how these models play these games. AI is becoming a constant in human interaction and examining how these models portray fairness in game play can give us some insight into how AI makes decisions. Over 101 rounds of the dictator game, I conclude that AI has a strong sense of fairness that is dependant of it it deems the person it is playing with as trustworthy, framing has a strong effect on how much AI gives a recipient when designated the trustee, and there may be evidence that AI experiences inequality aversion just as humans.
PromptGuard: An Orchestrated Prompting Framework for Principled Synthetic Text Generation for Vulnerable Populations using LLMs with Enhanced Safety, Fairness, and Controllability
Posts and Telecommunications Institute of Technology（邮电技术研究所）；Hanoi Architectural University（河内建筑大学）
Prompting Fairness: Integrating Causality to Debias Large Language Models
Google DeepMind（谷歌DeepMind）；Carnegie Mellon University（卡内基梅隆大学）；University of Maryland, College Park（马里兰大学帕克分校）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of California, Santa Cruz（加州大学圣克鲁兹分校）
