Assessing Large Language Models' ability to predict how humans balance self-interest and the interest of others
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866917591052713984 |
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| author | Capraro, Valerio Di Paolo, Roberto Pizziol, Veronica |
| author_facet | Capraro, Valerio Di Paolo, Roberto Pizziol, Veronica |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_12776 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Assessing Large Language Models' ability to predict how humans balance self-interest and the interest of others Capraro, Valerio Di Paolo, Roberto Pizziol, Veronica General Economics Economics Artificial Intelligence Computers and Society Computer Science and Game Theory 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. |
| title | Assessing Large Language Models' ability to predict how humans balance self-interest and the interest of others |
| topic | General Economics Economics Artificial Intelligence Computers and Society Computer Science and Game Theory |
| url | https://arxiv.org/abs/2307.12776 |