Assessing Large Language Models' ability to predict how humans balance
self-interest and the interest of others
- URL: http://arxiv.org/abs/2307.12776v3
- Date: Fri, 16 Feb 2024 12:38:53 GMT
- Title: Assessing Large Language Models' ability to predict how humans balance
self-interest and the interest of others
- Authors: Valerio Capraro, Roberto Di Paolo, Veronica Pizziol
- Abstract summary: Generative artificial intelligence (AI) holds enormous potential to revolutionize decision-making processes.
By leveraging generative AI, humans can benefit from data-driven insights and predictions.
However, for AI to be a reliable assistant for decision-making it is crucial that it is able to capture the balance between self-interest and the interest of others.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Generative artificial intelligence (AI) holds enormous potential to
revolutionize decision-making processes, from everyday to high-stake scenarios.
By leveraging generative AI, humans can benefit from data-driven insights and
predictions, enhancing their ability to make informed decisions that consider a
wide array of factors and potential outcomes. However, as many decisions carry
social implications, for AI to be a reliable assistant for decision-making it
is crucial that it is able to capture the balance between self-interest and the
interest of others. We investigate the ability of three of the most advanced
chatbots to predict dictator game decisions across 108 experiments with human
participants from 12 countries. We find that only GPT-4 (not Bard nor Bing)
correctly captures qualitative behavioral patterns, identifying three major
classes of behavior: self-interested, inequity-averse, and fully altruistic.
Nonetheless, GPT-4 consistently underestimates self-interest and
inequity-aversion, while overestimating altruistic behavior. This bias has
significant implications for AI developers and users, as overly optimistic
expectations about human altruism may lead to disappointment, frustration,
suboptimal decisions in public policy or business contexts, and even social
conflict.
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