fairness

This arXiv paper finds that language models exhibit a 'Women-Are-Wonderful' bias in blame attribution, showing both leniency toward women being judged and substantially harsher judgments when conduct affects a woman, based on 11,270 counterfactual conflicts from r/AmItheAsshole across four models.

The paper proves that group-blind recommendation policies incur linear regret when serving intersecting demographic groups, and proposes three group-aware UCB-based algorithms with sublinear regret guarantees, evaluated on Goodreads and BoardGameGeek datasets.

提出了治理感知联邦学习框架GAFL，为多智能体AI系统在协作训练中加入证据验证、公平性评估、策略执行与防篡改审计日志，并在跨孤岛原型上验证了其对伪造证据与恶意更新的识别能力。

This paper introduces the concept of 'disparity laundering,' showing that clinical multi-agent LLM teams do not eliminate demographic bias but instead carry it through into consensus decisions, with interventions only working when counterfactual versions are made visible and pooled during deliberation.

A survey and interview study of healthcare professionals examining how job roles and perspectives shape views on algorithmic bias causes, fairness criteria, and bias mitigation in healthcare ML systems, finding significant variation across clinical and administrative roles.

This paper evaluates cross-lingual fairness of edit-based homoglyph and invisible-character watermarking across 40 language varieties in 24 scripts, finding that removing tokenizers does not eliminate inequity since script, not resource level, drives 96% of capacity variation.

This paper adapts the Context Association Test to show that GPT-4o exhibits gendered ADHD subtype associations in self-diagnostic prompts, with explicit demographic cues apparently triggering over-correction while implicit cues reveal the model's default bias.

This paper explores activation steering for automatic speech recognition (ASR), finding that supervised optimization of steering vectors improves both overall ASR performance and demographic fairness across gender, age, and dialect, while also enabling few-shot or zero-shot adaptation using acoustically similar speakers.

AllenAI introduces FairStress, a benchmark measuring demographic fairness in LLM decision-making beyond final choices, testing how easily adversarial arguments make models change decisions across demographic groups in high-stakes domains like hiring, lending, and healthcare.

The paper introduces calibrated accommodation pairs (CAP), a preference dataset that rewards chat models for matching users' dialect while preserving Standard English, and audits how SFT, DPO, and RL stages change a model's tendency to accommodate informal dialect cues across 10 open model lineages.

This paper introduces Plausibility-EFG, a group-level disparity metric for measuring explanation plausibility gaps across demographic groups in hate speech detection, and proposes Fair-REF, an adversarial rationale regularization strategy that reduces explanation disparities while maintaining classification performance.

该论文提出了一个以用户为中心的审计框架，用于区分基准内容的贡献者（源用户）与评估结果所代表的群体（目标用户），并在16个评估资源上发现美国中心化惯例占据主导、文档中普遍存在源-目标用户语境差距，且评分指标会显著影响性能衡量。

This paper investigates systematic pro-AI bias in LLMs, finding that models disproportionately recommend AI-related options in advice-seeking queries, overestimate AI job salaries, and that 'Artificial Intelligence' holds unusually central internal representations regardless of framing, with proprietary models showing stronger bias.

A University of Amsterdam study combines RL-based Multiple Instance Learning, adversarial debiasing, and preference-conditioned hypernetworks for student-at-risk prediction, finding that fairness objectives can be added to an interpretable RL-MIL pipeline but preference conditioning alone fails to yield controllable trade-offs between performance and Equalized Odds due to mode collapse.

The paper introduces Collective Bias Mitigation (CBM), a framework that reduces LLM bias by fine-tuned model behavior and knowledge sharing among diverse LLMs, using Debating and Committee routing topologies to balance bias reduction and inference cost.

The paper shows that in speech LLM adaptation, random training-seed variability can outweigh audio compression choices in determining demographic fairness gaps, arguing that single-run fairness reports are unreliable. Variance decomposition attributes 85.3% of Fair-Speech ethnicity gap variation to seed effects, and effects persist even after scaling the adaptation data.

This paper introduces Fable, a controlled dataset of 190,911 English prompt variants derived from 174K real user prompts, and evaluates 34 open-weight LLMs to show that models mirror higher-level rhetorical and lexical qualities of non-native speakers' prompts, producing lower-quality and less-fluent responses for less fluent users.

A HEC Montréal/MILA paper proposes fair policy optimization for major-minor weakly coupled MDPs, replacing the utilitarian objective with monotone concave fairness functions and introducing a count-proportion-based deep RL approach with a priority-based sampler, validated on machine replacement and NYC taxi pricing/relocation tasks.

The study reveals that thinking in reasoning language models has an asymmetric dual effect on counterfactual fairness: it resolves some biases but creates more, with the latter outnumbering the former by about 5× across models and datasets.

This paper introduces Inquesto Score, a reliability protocol for voice agents that measures interaction success by defining failure events and severity levels, with diagnostic views for behavior and acoustic robustness.