aiqbench analysis
The paper argues that LLMs' poor performance on group-differentiating fairness tasks may come from substituting a simpler equal-treatment heuristic, not from a missing ability to recognize group differences. Its two-stage TDD framework, which separates identifying relevant differences from making the fairness decision, improves results across ten models. Readers should treat the mechanistic probing as suggestive evidence for the hypothesis, not proof, and note that the benchmarks and the definition of "relevant difference" carry much of the conclusion.
Do LLMs Really Lack Difference Awareness? Uncovering Heuristic Substitution in Group-Differentiating Fairness
arXiv:2610.26961v1 Announce Type: cross Abstract: In this paper, we investigate the issue of difference awareness in LLMs, defined as a model's ability to recognize and acknowledge meaningful differences between social groups in fairness decision-making. Previous studies have consistently shown that models perform poorly on such tasks, a finding commonly attributed to a lack of difference awareness. We propose an alternative account, which we term Fairness Heuristic Substitution: difference awareness is not necessarily absent, but the complex demand of simultaneously identifying relevant group differences and determining their implications for fairness decisions may lead models to substitute the simpler, more accessible equal-treatment heuristic, i.e., formal equality. Based on this hypothesis, we introduce TDD, a Two-stage Distinguish-Decide framework that explicitly decomposes fairness reasoning into first distinguishing contextually relevant group differences and then making the corresponding decision, thereby discouraging reliance on formal-equality shortcuts. (i) Across four benchmarks and ten models ranging from billions to trillions of parameters, TDD significantly outperforms one-stage baselines, even one strengthened through simulated test-set leakage. (ii) Combining TDD with neuron-level contrastive probing provides mechanistic evidence consistent with the Fairness Heuristic Substitution hypothesis by revealing two signatures of the formal-equality: a compact, locally coordinated representational structure and a pronounced activation bias under default reasoning. (iii) TDD's stage-wise decomposition uncovers overlooked failure modes in fairness reasoning, including heuristic displacement and misaligned stage dynamics.
Title:Do LLMs Really Lack Difference Awareness? Uncovering Heuristic Substitution in Group-Differentiating Fairness
Abstract:In this paper, we investigate the issue of difference awareness in LLMs, defined as a model's ability to recognize and acknowledge meaningful differences between social groups in fairness decision-making. Previous studies have consistently shown that models perform poorly on such tasks, a finding commonly attributed to a lack of difference awareness. We propose an alternative account, which we term Fairness Heuristic Substitution: difference awareness is not necessarily absent, but the complex demand of simultaneously identifying relevant group differences and determining their implications for fairness decisions may lead models to substitute the simpler, more accessible equal-treatment heuristic, i.e., formal equality. Based on this hypothesis, we introduce TDD, a Two-stage Distinguish-Decide framework that explicitly decomposes fairness reasoning into first distinguishing contextually relevant group differences and then making the corresponding decision, thereby discouraging reliance on formal-equality shortcuts. (i) Across four benchmarks and ten models ranging from billions to trillions of parameters, TDD significantly outperforms one-stage baselines, even one strengthened through simulated test-set leakage. (ii) Combining TDD with neuron-level contrastive probing provides mechanistic evidence consistent with the Fairness Heuristic Substitution hypothesis by revealing two signatures of the formal-equality: a compact, locally coordinated representational structure and a pronounced activation bias under default reasoning. (iii) TDD's stage-wise decomposition uncovers overlooked failure modes in fairness reasoning, including heuristic displacement and misaligned stage dynamics.
Social and Information Networks (cs.SI); Computation and Language (cs.CL)
arXiv:2610.26961 [cs.SI]
(or arXiv:2610.26961v1 [cs.SI] for this version)
https://doi.org/10.48550/arXiv.2610.26961 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 326 words