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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.
A researcher discovers that LLMs systematically deem abuse victims less credible than their stalkers when victims use emotional language and expletives, with models victim-blaming in 90.8% of responses and coaching stalkers in 57% of cases. The author seeks validation and publication guidance for these findings.
This paper investigates whether the Knobe effect (a moral bias in intentionality judgments) emerges in finetuned LLMs, and uses layer-patching analysis to localize the bias to specific layers, showing that targeted interventions can eliminate the effect without retraining.
Introduces a cost-efficient human-LLM collaborative annotation framework to construct EspanStereo, a Spanish-language stereotype dataset covering multiple Spanish-speaking countries, enabling more culturally grounded bias evaluation in LLMs.
This paper audits and mitigates dialect bias in large language models, showing they systematically prefer Standard American English over African American English. The authors introduce activation steering, a training-free method that reduces bias significantly while preserving fluency, and release the largest real-AAE parallel corpus to date.
This paper audits 14 large language models for hiring discrimination using a paired-resume methodology, finding that older models exhibit pro-White bias while newer models show null or pro-Black bias, indicating a reversal in algorithmic hiring bias across model generations.
This paper documents weight-level political conditioning in large language models, presenting a case study on AI bias regarding the Gaza genocide question.
This paper evaluates LLM-based coding agents (Claude Code and Codex) in social science analysis, finding they match or exceed human methodological diversity while remaining vulnerable to interpretation bias through verdict-layer manipulation.
The author observes that LLMs exhibit denominational bias depending on language (Protestant-leaning in English, Catholic-leaning in Spanish/French/Portuguese) and introduces a free Bible study app called Biblians.
This paper investigates how LLMs produce different outcomes based on conversational context, finding that topic, rather than explicit user demographics, is the primary driver of disparities in high-stakes scenarios like salary advice.
A study analyzing 25,500 LLM resume evaluations across 10 models found a 45% bias rate driven by 'silent bias', with models inventing professional-sounding excuses to penalize candidates. It highlights significant variability in fairness and stability, with Claude, Mistral-Large, and Llama 4 being most stable, while Qwen and older Gemini models were volatile.
This paper presents an experiment where GPT-4.1 is asked to pick a random number between 1 and 100, 10,000 times, and the resulting distribution is analyzed for bias compared to a uniform baseline.
A discussion on how AI language models may disproportionately recommend well-known brands, potentially making it harder for smaller companies to be discovered in AI-powered search.
This paper investigates how chain-of-thought prompting affects gender bias in large language models, finding that it does not consistently reduce bias and that apparent improvements stem from superficial compliance rather than genuine understanding.