@inproceedings{kim-etal-2026-machine,
title = "Machine Behavior in Relational Moral Dilemmas: Moral Rightness, Predicted Human Behavior, and Model Decisions",
author = "Kim, Jiseon and
Kwon, Jea and
Vecchietti, Luiz Felipe and
Dong, Wenchao and
Kim, Jaehong and
Cha, Meeyoung",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1547/",
pages = "30938--30955",
ISBN = "979-8-89176-395-1",
abstract = "Human moral judgment is context-dependent and changes based on interpersonal relationships. As large language models (LLMs) increasingly serve as decision-support systems, it is critical to understand if they encode these social nuances. We characterize LLM behavior using the Whistleblower{'}s Dilemma, systematically varying two experimental factors: crime severity and relational closeness. Our study compares three evaluative perspectives: (1) moral rightness (general prescriptive norms), (2) predictive human behavior (how models expect people to navigate social situations), and (3) models' own decision-making. By analyzing the reasoning processes, we find a clear cross-perspective divergence: moral rightness remains consistently fairness-oriented, while predicted human behavior shifts with relational context toward loyalty. Crucially, the model decisions mirror moral rightness judgments, rather than their behavioral predictions. This cross-perspective inconsistency suggests that LLM decision-making favors abstract rules over the social sensitivity found in their internal modeling, potentially producing conflicting expectations in real-world deployments."
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<abstract>Human moral judgment is context-dependent and changes based on interpersonal relationships. As large language models (LLMs) increasingly serve as decision-support systems, it is critical to understand if they encode these social nuances. We characterize LLM behavior using the Whistleblower’s Dilemma, systematically varying two experimental factors: crime severity and relational closeness. Our study compares three evaluative perspectives: (1) moral rightness (general prescriptive norms), (2) predictive human behavior (how models expect people to navigate social situations), and (3) models’ own decision-making. By analyzing the reasoning processes, we find a clear cross-perspective divergence: moral rightness remains consistently fairness-oriented, while predicted human behavior shifts with relational context toward loyalty. Crucially, the model decisions mirror moral rightness judgments, rather than their behavioral predictions. This cross-perspective inconsistency suggests that LLM decision-making favors abstract rules over the social sensitivity found in their internal modeling, potentially producing conflicting expectations in real-world deployments.</abstract>
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%0 Conference Proceedings
%T Machine Behavior in Relational Moral Dilemmas: Moral Rightness, Predicted Human Behavior, and Model Decisions
%A Kim, Jiseon
%A Kwon, Jea
%A Vecchietti, Luiz Felipe
%A Dong, Wenchao
%A Kim, Jaehong
%A Cha, Meeyoung
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F kim-etal-2026-machine
%X Human moral judgment is context-dependent and changes based on interpersonal relationships. As large language models (LLMs) increasingly serve as decision-support systems, it is critical to understand if they encode these social nuances. We characterize LLM behavior using the Whistleblower’s Dilemma, systematically varying two experimental factors: crime severity and relational closeness. Our study compares three evaluative perspectives: (1) moral rightness (general prescriptive norms), (2) predictive human behavior (how models expect people to navigate social situations), and (3) models’ own decision-making. By analyzing the reasoning processes, we find a clear cross-perspective divergence: moral rightness remains consistently fairness-oriented, while predicted human behavior shifts with relational context toward loyalty. Crucially, the model decisions mirror moral rightness judgments, rather than their behavioral predictions. This cross-perspective inconsistency suggests that LLM decision-making favors abstract rules over the social sensitivity found in their internal modeling, potentially producing conflicting expectations in real-world deployments.
%U https://aclanthology.org/2026.findings-acl.1547/
%P 30938-30955
Markdown (Informal)
[Machine Behavior in Relational Moral Dilemmas: Moral Rightness, Predicted Human Behavior, and Model Decisions](https://aclanthology.org/2026.findings-acl.1547/) (Kim et al., Findings 2026)
ACL
- Jiseon Kim, Jea Kwon, Luiz Felipe Vecchietti, Wenchao Dong, Jaehong Kim, and Meeyoung Cha. 2026. Machine Behavior in Relational Moral Dilemmas: Moral Rightness, Predicted Human Behavior, and Model Decisions. In Findings of the Association for Computational Linguistics: ACL 2026, pages 30938–30955, San Diego, California, United States. Association for Computational Linguistics.