The Moral Mind(s) of Large Language Models

Avner Seror

No 2433, AMSE Working Papers from Aix-Marseille School of Economics, France

Abstract: As large language models (LLMs) become integrated to decision-making across various sectors, a key question arises: do they exhibit an emergent "moral mind" - a consistent set of moral principles guiding their ethical judgments - and is this reasoning uniform or diverse across models? To investigate this, we presented about forty different models from the main providers with a large array of structured ethical scenarios, creating one of the largest datasets of its kind. Our rationality tests revealed that at least one model from each provider demonstrated behavior consistent with stable moral principles, effectively acting as approximately optimizing a utility function encoding ethical reasoning. We identified these utility functions and observed a notable clustering of models around neutral ethical stances. To investigate variability, we introduced a novel non-parametric permutation approach, revealing that the most rational models shared 59% to 76% of their ethical reasoning patterns. Despite this shared foundation, differences emerged: roughly half displayed greater moral adaptability, bridging diverse perspectives, while the remainder adhered to more rigid ethical structures.

Keywords: Decision Theory; revealed preference; Rationality; artificial intelligence; LLM; PSM. (search for similar items in EconPapers)
JEL-codes: C44 C9 D9 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2024-11
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp and nep-upt
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Persistent link: https://EconPapers.repec.org/RePEc:aim:wpaimx:2433

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