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Safety#LLM🔬 ResearchAnalyzed: Jan 10, 2026 08:41

Identifying and Mitigating Bias in Language Models Against 93 Stigmatized Groups

Published:Dec 22, 2025 10:20
1 min read
ArXiv

Analysis

This ArXiv paper addresses a crucial aspect of AI safety: bias in language models. The research focuses on identifying and mitigating biases against a large and diverse set of stigmatized groups, contributing to more equitable AI systems.
Reference

“The research focuses on 93 stigmatized groups.”

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Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 09:47

Can AI Understand What We Cannot Say? Measuring Multilevel Alignment Through Abortion Stigma Across Cognitive, Interpersonal, and Structural Levels

Published:Dec 15, 2025 09:50
1 min read
ArXiv

Analysis

This article explores the ability of AI to understand complex social phenomena, specifically focusing on abortion stigma. The research likely investigates how well AI models can align with human understanding across different levels of analysis (cognitive, interpersonal, and structural). The use of abortion stigma as a case study suggests a focus on sensitive and nuanced topics, potentially highlighting the challenges and limitations of AI in dealing with complex social issues.
Reference

“The article's focus on 'measuring multilevel alignment' suggests a quantitative or computational approach to assessing AI's understanding. The choice of abortion stigma as a subject matter implies a focus on sensitive and potentially controversial topics.”

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