Cheung, V., Maier, M., & Lieder, F. (2025).
PNAS, 122(25).
Abstract
As large language models (LLMs) become more widely used, people increasingly rely on them to make or advise on moral decisions. Some researchers even propose using LLMs as participants in psychology experiments. It is, therefore, important to understand how well LLMs make moral decisions and how they compare to humans. We investigated these questions by asking a range of LLMs to emulate or advise on people’s decisions in realistic moral dilemmas. In Study 1, we compared LLM responses to those of a representative U.S. sample (N = 285) for 22 dilemmas, including both collective action problems that pitted self-interest against the greater good, and moral dilemmas that pitted utilitarian cost–benefit reasoning against deontological rules. In collective action problems, LLMs were more altruistic than participants. In moral dilemmas, LLMs exhibited stronger omission bias than participants: They usually endorsed inaction over action. In Study 2 (N = 474, preregistered), we replicated this omission bias and documented an additional bias: Unlike humans, most LLMs were biased toward answering “no” in moral dilemmas, thus flipping their decision/advice depending on how the question is worded. In Study 3 (N = 491, preregistered), we replicated these biases in LLMs using everyday moral dilemmas adapted from forum posts on Reddit. In Study 4, we investigated the sources of these biases by comparing models with and without fine-tuning, showing that they likely arise from fine-tuning models for chatbot applications. Our findings suggest that uncritical reliance on LLMs’ moral decisions and advice could amplify human biases and introduce potentially problematic biases.
Significance
How will people’s increasing reliance on large language models (LLMs) influence their opinions about important moral and societal decisions? Our experiments demonstrate that the decisions and advice of LLMs are systematically biased against doing anything, and this bias is stronger than in humans. Moreover, we identified a bias in LLMs’ responses that has not been found in people. LLMs tend to answer “no,” thus flipping their decision/advice depending on how the question is worded. We present some evidence that suggests both biases are induced when fine-tuning LLMs for chatbot applications. These findings suggest that the uncritical reliance on LLMs could amplify and proliferate problematic biases in societal decision-making.
Here are some thoughts:
The study investigates how Large Language Models (LLMs) and humans differ in their moral decision-making, particularly focusing on cognitive biases such as omission bias and yes-no framing effects. For psychologists, understanding these biases helps clarify how both humans and artificial systems process dilemmas. This knowledge can inform theories of moral psychology by identifying whether certain biases are unique to human cognition or emerge in artificial systems trained on human data.
Psychologists are increasingly involved in interdisciplinary work related to AI ethics, particularly as it intersects with human behavior and values. The findings demonstrate that LLMs can amplify existing human cognitive biases, which raises concerns about the deployment of AI systems in domains like healthcare, criminal justice, and education where moral reasoning plays a critical role. Psychologists need to understand these dynamics to guide policies that ensure responsible AI development and mitigate risks.
