# Fair Like Us? Auditing LLM Alignment in Resource Allocation

arXiv: https://arxiv.org/abs/2609.29692 · HTML: https://mlsanity.com/paper/2609.29692/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 28 of its 76 references that are in the map.
- First posted on arXiv 31 Aug 2026.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Human values and AI feedback alignment](https://mlsanity.com/alignment/human-values-and-ai-feedback-alignment/)

## Builds on (most cited first, 28 of 28)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://mlsanity.com/paper/2302.00093/) (2023)
- [Aligning AI With Shared Human Values](https://mlsanity.com/paper/2008.02275/) (2020)
- [Evaluating the Moral Beliefs Encoded in LLMs](https://mlsanity.com/paper/2307.14324/) (2023)
- [Do LLMs exhibit human-like response biases? A case study in survey design](https://mlsanity.com/paper/2311.04076/) (2023)
- [A Categorical Archive of ChatGPT Failures](https://mlsanity.com/paper/2302.03494/) (2023)
- [Debiasing Vision-Language Models via Biased Prompts](https://mlsanity.com/paper/2302.00070/) (2023)
- [Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)](https://mlsanity.com/paper/2510.22954/) (2025)
- [Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?](https://mlsanity.com/paper/2211.13972/) (2022)
- [Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies](https://mlsanity.com/paper/2112.11471/) (2021)
- [Game-theoretic LLM: Agent Workflow for Negotiation Games](https://mlsanity.com/paper/2411.05990/) (2024)
- [Cultivating Pluralism In Algorithmic Monoculture: The Community Alignment Dataset](https://mlsanity.com/paper/2507.09650/) (2025)
- [The Homogenizing Effect of Large Language Models on Human Expression and Thought](https://mlsanity.com/paper/2508.01491/) (2025)
- [Fair Public Decision Making](https://mlsanity.com/paper/1611.04034/) (2016)
- [Strategic Tradeoffs Between Humans and AI in Multi-Agent Bargaining](https://mlsanity.com/paper/2509.09071/) (2025)
- [Understanding LLM Agent Behaviours via Game Theory: Strategy Recognition, Biases and Multi-Agent Dynamics](https://mlsanity.com/paper/2512.07462/) (2025)
- [Distributive Fairness in Large Language Models: Evaluating Alignment with Human Values](https://mlsanity.com/paper/2502.00313/) (2025)
- [Many LLMs Are More Utilitarian Than One](https://mlsanity.com/paper/2507.00814/) (2025)
- [Strategic Algorithmic Monoculture: Experimental Evidence from Coordination Games](https://mlsanity.com/paper/2604.09502/) (2026)
- [Aligning Large Language Model Agents with Rational and Moral Preferences: A Supervised Fine-Tuning Approach](https://mlsanity.com/paper/2507.20796/) (2025)
- [Evaluating the application of NLP tools in mainstream participatory budgeting processes in Scotland](https://mlsanity.com/paper/2111.11766/) (2021)
- [The Illusion of Rationality: Tacit Bias and Strategic Dominance in Frontier LLM Negotiation Games](https://mlsanity.com/paper/2512.09254/) (2025)
- [The Judgment-Consequence Gap: LLM Moral Reasoning in Healthcare Decisions](https://mlsanity.com/paper/2608.05583/) (2026)
- [Exploring Large Language Model Agents for Piloting Social Experiments](https://mlsanity.com/paper/2508.08678/) (2025)
- [Can LLMs Help Allocate Public Health Resources? A Case Study on Childhood Lead Testing](https://mlsanity.com/paper/2511.18239/) (2025)
- [Bridging Theory and Perception in Fair Division: A Study on Comparative and Fair Share Notions](https://mlsanity.com/paper/2505.10433/) (2025)

## Abstract (arXiv)

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.