Prompting Fairness: Artificial Intelligence as Game Players
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Abstract (arXiv)
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.
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References · 4
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2023Large Language Models Cannot Self-Correct Reasoning Yetcited by 1,017
- 2023Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Modelscited by 560
- 2023Gender bias and stereotypes in Large Language Modelscited by 187
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