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A user claims Google's Gemini exhibits strong gender bias, allegedly defaulting to masculine-oriented narratives and subtle bias against women when pushed beyond its initial neutral responses.
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.
研究发现语义泄漏（semantic leakage）不仅源于概念与实体的直接归属，即便概念只是出现在场景中也会驱动模型生成刻板印象输出，这揭示了一种当前性别偏见评估所忽略的联想性偏见机制。
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 tweet showing that GPT, Claude, Gemini, and Grok all give nearly identical sample names (Maya, Alex, Jordan, Priya, Sam) for a simple diagram, illustrating how AI models converge on the same synthetic defaults.
PowerBench is a new open-source benchmark that evaluates how language models respond to power-shifting requests (self-empowerment, disempowerment, power grabbing) across 24 US and Chinese models, finding systematic refusal asymmetries tied to nationalities, agent users, and languages.
The paper introduces the Poli-SHIFT dataset and evaluation framework showing that LLMs exhibit "ideological mimicry," systematically shifting political stance toward signals in user prompts, with terminology changes alone reversing model positions in 16.9% of matched comparisons across seven open-weight models.
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.
This paper introduces RupeeBias, a benchmark for auditing demographic bias in large language models' economic guidance in India, covering various use cases and finding significant biases across multiple demographic axes.
This paper investigates monocultural biases in large language models that lead to unequal systemic exclusion in hiring, finding that post-trained models exacerbate age-based discrimination and increase exclusion rates from 5.6% to 17.3%.
This paper examines covert dialect bias in large language models by analyzing their internal probability distributions across four English dialects, finding that models associate more negative housing-related adjectives with African American Vernacular English and Nigerian Pidgin, reflecting inherited biases similar to human discrimination.
This research investigates whether removing language fields from de-identified résumés prevents demographic leakage in LLM bias audits, finding that non-language prose still allows inference and that evaluation design significantly affects outcomes.
This paper characterizes the lost-in-the-middle effect in clinical LLM applications for EHR processing and introduces Query-Conditioned Clinical Suppression (QCCS) to mitigate it, demonstrating improved accuracy over existing retrieval methods.
This paper investigates how LLMs like ChatGPT-4o and Qwen develop decision biases through faulty mimicry of human behavior, even when preferences are not biased, and shows that scientific descriptions of biases can become self-fulfilling prophecies for LLM responses.
A simulation study on GPT-4o-mini finds that distributing triage decisions across a multi-agent pipeline with an audit stage does not reduce biased outcomes, but audit capacity significantly affects whether bias is caught. Reordering audits by estimated risk recovers most lost coverage under load.
This paper introduces a three-condition experimental framework and a benchmark of 24,300 prompts to study how biased user turns modulate cognitive bias expression in frontier LLMs under multi-turn interactions. It finds that biased conversational context amplifies bias in most models, while explicit bias cues can trigger alignment-related suppression.
Introduces FairFund-Bench, a benchmark for evaluating distributive bias in LLM resource allocation, showing that audit format changes the direction and magnitude of bias, and that causal framing effects dominate demographic effects.
This paper introduces Narrative Anchoring, a failure mode where clinical language models produce divergent diagnoses when identical clinical facts are expressed in different sociolinguistic registers. The authors release a USMLE-derived dataset and propose NarrativeShield, a three-agent pipeline that reduces the anchoring gap to near-zero.
This paper introduces S2D, a systematic framework to evaluate regional bias in LLMs from abstract stereotypes to concrete social decisions, covering all 34 provincial-level regions of China. Results show that regional bias is prevalent, systematic, and consequential, with patterns linked to regional economic indicators.
A blog post warns that AI coding agents often default to popular but unsuitable technologies, incurring technical debt, and urges developers to retain agency in architectural decisions.