arXiv AI
Jul 3
World Wide Models: Literary Tools for Cultural AI
arXiv:2607. 02369v1 Announce Type: cross Abstract: LLMs stage a new form of cultural encounter that is massive, automated, and monolingual.
Insidious by Design: Implications of Large Language Model algorithmic bias for the Global South
arXiv:2606. 28333v1 Announce Type: cross Abstract: \begin{quote} The biases in Large Language Models' (LLMs) outputs remain inadequately theorised, particularly from the perspective of the Global South.
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arXiv AI
Aug 13
Large Language Models Reproduce Racial Stereotypes When Used for Text Annotation
arXiv:2603. 13891v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for automated text annotation in tasks ranging from academic research to content moderation and hiring.
arXiv AI
Aug 20
Computational Orientalism: Measuring Structural Discourse Bias in Large Language Models Using the Middle East Cultural Sensitivity Score (MECSS)
The paper introduces the Middle East Cultural Sensitivity Score (MECSS) to quantify Orientalist bias in large language models, converting Said’s seven Orientalist operations into measurable dimensions. Using 280 conversations, it finds that GPT‑4 and Falcon3‑7B‑Instruct systematically reproduce Orientalist patterns, with Falcon scoring higher despite being regionally built. The study highlights that geographic origin alone does not mitigate bias and identifies a new failure mode, "Said‑washing," present in 87.9% of GPT‑4 interactions.
arXiv Machine Learning
Jul 31
Sympathetic Framing: Evaluating AI Alignment across Sociodemographic Groups
arXiv:2607. 27232v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly shaping how we consume information and form our worldview.
arXiv AI
Jun 3
Generating the Modal Worker: A Cross-Model Audit of Race and Gender in LLM-Generated Personas Across 41 Occupations
arXiv:2510. 21011v3 Announce Type: replace-cross Abstract: As generative AI tools are increasingly used to portray people in professional roles, understanding their racial and gender representational biases is critical.
arXiv AI
Sep 10
The Emergence of Social Science of Large Language Models
arXiv:2509.24877v3 Announce Type: replace Abstract: The social science of large language models (LLMs) examines how these systems evoke mind attributions, interact with one another, and transform hum...
arXiv AI
Jun 9
Neutrality Bites: Gender Representation in AI-Generated Animal Stories
arXiv:2606. 07969v1 Announce Type: cross Abstract: Gender bias in AI-generated stories is a well-documented problem.
arXiv AI
Jul 14
The Paternalistic Filter: Epistemic Injustice and Differential Refusal in LLM-Mediated History Education for Marginalized Romanian Students
arXiv:2607. 11292v1 Announce Type: cross Abstract: As Large Language Models (LLMs) are increasingly deployed as conversational tutors, they risk institutionalizing systemic inequalities.
arXiv AI
Sep 17
From a River in Gilead to the Inference Distributions of Large Language Models: Covert Dialect Bias and Linguistic Profiling at Scale
The paper investigates covert dialect bias in large language models (LLMs) by analyzing how internal probability distributions associate different English varieties—Standard American English, African American Vernacular English, Nigerian Standard English, and Nigerian Pidgin—with housing-related adjectives. Using 260 meaning‑matched sentence quadruples and log‑probability scoring across ten open‑weight LLMs, the study finds that AAVE and NP are consistently linked to more negative adjectives than SAE, with NP experiencing the greatest penalty. The bias varies by context and stereotype cluster, and Nigerian Standard English shows a context‑dependent shift, being favored in formal tenant screening but penalized in more socially proximate scenarios.
arXiv AI
Jul 7
Large Language Models Develop Novel Social Biases Through Adaptive Exploration
arXiv:2511. 06148v4 Announce Type: replace-cross Abstract: As large language models (LLMs) are adopted into frameworks that grant them the capacity to make real decisions, it is increasingly important to ensure that they are unbiased.
arXiv Computation and Language
3d ago
Anthropomorphism in the age of Large Language Models: An overview of potential risks and mitigations
arXiv:2609.38486v1 Announce Type: cross Abstract: Large Language Models (LLMs) and more broadly Artificial Intelligence (AI) systems are often described and understood in human-like terms, a phenomen...
arXiv AI
Aug 28
How LLMs Distort Our Written Language
Large language models (LLMs) are widely used to assist writing, but this study shows they alter both tone and meaning of human text. A user study found that heavy LLM use increased neutral essays by nearly 70% and made writers feel less creative and less in their voice. Even when prompted to make only grammar edits, LLMs changed the semantic content of essays and produced AI-generated scientific reviews that were less focused on clarity and significance and scored higher on average.