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FairStress: A deeper evaluation of fairness in LLM decision-making
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arXiv Machine Learning
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Equality, Equity, and Causality in Fairness Research: A Commentary on Cheng (2026)
arXiv:2607. 17679v1 Announce Type: cross Abstract: This is an invited commentary on the Psychometrika focus article "Fairness Issues and Evaluation in Psychometrics and AI/ML: What Can We Learn from Each Field?
arXiv Machine Learning
Jun 2
Beyond Procedure: Substantive Fairness in Conformal Prediction
arXiv:2602. 16794v2 Announce Type: replace-cross Abstract: Conformal prediction (CP) offers distribution-free uncertainty quantification for machine learning models, yet its interplay with fairness in downstream decision-making remains underexplored.
arXiv Computation and Language
Oct 21
Do LLMs Really Lack Difference Awareness? Uncovering Heuristic Substitution in Group-Differentiating Fairness
The paper examines whether large language models (LLMs) truly lack awareness of meaningful differences between social groups in fairness decisions. It proposes that instead of lacking difference awareness, LLMs often substitute a simpler equal‑treatment heuristic, a phenomenon called Fairness Heuristic Substitution. The authors introduce a Two‑stage Distinguish‑Decide (TDD) framework that separates the identification of relevant group differences from the fairness decision, and show that TDD outperforms one‑stage baselines across multiple benchmarks and models, while also revealing mechanistic evidence for the heuristic substitution hypothesis.
arXiv AI
Sep 25
Fair Like Us? Auditing LLM Alignment in Resource Allocation
The paper "Fair Like Us? Auditing LLM Alignment in Resource Allocation" presents a method for evaluating how large language models reason about fairness in the allocation of scarce, indivisible resources. It compares LLMs’ first‑person fairness judgments with human responses across various scenarios, finding that models tend to favor stricter fairness constraints, exhibit more self‑interested behavior, and are sensitive to framing. The study also shows that current fine‑tuning datasets struggle to align LLM judgments with human ones.
Hugging Face Trending Papers
Aug 17
Position: Fairness Failure in Generative Models is an Evaluation Problem
Despite groundbreaking advancements in generative models during the last decade, concerns about their lack of fairness, reinforcing societal inequalities and harming marginalized groups, remain under-...
arXiv Machine Learning
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Procedural Fairness in Multi-Agent Bandits
arXiv:2601. 10600v2 Announce Type: replace-cross Abstract: In the context of multi-agent multi-armed bandits (MA-MAB), fairness is often reduced to outcomes: maximizing welfare, reducing inequality, or balancing utilities.