@inproceedings{han-etal-2026-fair,
title = "Fair Like Us? Auditing {LLM} Alignment in Resource Allocation",
author = "Han, Qishen and
Hosseini, Hadi and
Kavner, Joshua and
Khanna, Samarth and
Sikdar, Sujoy and
Xia, Lirong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.620/",
pages = "14223--14255",
ISBN = "979-8-89176-459-0",
abstract = "Fair allocation of scarce, indivisible resources is an important challenge in many societal problems. While there are several formal theories of fairness, no single definition can always be satisfied. As large language models (LLMs) are increasingly used to support decisions and act as agents, they raise new concerns about distributional justice: their judgments are not directly tied to any specific fairness framework and may violate key normative principles. In this work, we introduce a general method for evaluating fairness reasoning in LLMs. We study first-person fairness judgments across a broad set of models and compare them directly with human responses on matched scenarios and elicitation conditions. We find that LLMs tend to prefer stricter fairness constraints than humans, show more self-interested behavior, are sensitive to how information is framed, and are difficult to align with human judgments using fine-tuning with current datasets."
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<abstract>Fair allocation of scarce, indivisible resources is an important challenge in many societal problems. While there are several formal theories of fairness, no single definition can always be satisfied. As large language models (LLMs) are increasingly used to support decisions and act as agents, they raise new concerns about distributional justice: their judgments are not directly tied to any specific fairness framework and may violate key normative principles. In this work, we introduce a general method for evaluating fairness reasoning in LLMs. We study first-person fairness judgments across a broad set of models and compare them directly with human responses on matched scenarios and elicitation conditions. We find that LLMs tend to prefer stricter fairness constraints than humans, show more self-interested behavior, are sensitive to how information is framed, and are difficult to align with human judgments using fine-tuning with current datasets.</abstract>
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%0 Conference Proceedings
%T Fair Like Us? Auditing LLM Alignment in Resource Allocation
%A Han, Qishen
%A Hosseini, Hadi
%A Kavner, Joshua
%A Khanna, Samarth
%A Sikdar, Sujoy
%A Xia, Lirong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F han-etal-2026-fair
%X Fair allocation of scarce, indivisible resources is an important challenge in many societal problems. While there are several formal theories of fairness, no single definition can always be satisfied. As large language models (LLMs) are increasingly used to support decisions and act as agents, they raise new concerns about distributional justice: their judgments are not directly tied to any specific fairness framework and may violate key normative principles. In this work, we introduce a general method for evaluating fairness reasoning in LLMs. We study first-person fairness judgments across a broad set of models and compare them directly with human responses on matched scenarios and elicitation conditions. We find that LLMs tend to prefer stricter fairness constraints than humans, show more self-interested behavior, are sensitive to how information is framed, and are difficult to align with human judgments using fine-tuning with current datasets.
%U https://aclanthology.org/2026.emnlp-main.620/
%P 14223-14255
Markdown (Informal)
[Fair Like Us? Auditing LLM Alignment in Resource Allocation](https://aclanthology.org/2026.emnlp-main.620/) (Han et al., EMNLP 2026)
ACL
- Qishen Han, Hadi Hosseini, Joshua Kavner, Samarth Khanna, Sujoy Sikdar, and Lirong Xia. 2026. Fair Like Us? Auditing LLM Alignment in Resource Allocation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 14223–14255, Budapest, Hungary. Association for Computational Linguistics.