FairMindSim: Alignment of Behavior, Emotion, and Belief in Humans and LLM Agents Amid Ethical Dilemmas

TL;DR

This paper introduces FairMindSim, a simulation framework for aligning LLM and human moral behavior in ethical dilemmas, incorporating sociological insights and a new belief-reward model.

Contribution

It presents FairMindSim and the BREM model to enhance understanding of moral alignment in AI and humans during ethical conflicts.

Findings

GPT-4o shows stronger social justice tendencies

Humans exhibit a richer emotional spectrum

Emotions influence moral decision-making

Abstract

AI alignment is a pivotal issue concerning AI control and safety. It should consider not only value-neutral human preferences but also moral and ethical considerations. In this study, we introduced FairMindSim, which simulates the moral dilemma through a series of unfair scenarios. We used LLM agents to simulate human behavior, ensuring alignment across various stages. To explore the various socioeconomic motivations, which we refer to as beliefs, that drive both humans and LLM agents as bystanders to intervene in unjust situations involving others, and how these beliefs interact to influence individual behavior, we incorporated knowledge from relevant sociological fields and proposed the Belief-Reward Alignment Behavior Evolution Model (BREM) based on the recursive reward model (RRM). Our findings indicate that, behaviorally, GPT-4o exhibits a stronger sense of social justice, while humans display a richer range of emotions. Additionally, we discussed the potential impact of emotions on behavior. This study provides a theoretical foundation for applications in aligning LLMs with altruistic values.