34 citations · 35 across the 5 of their papers we have counts for
6 papers
Greater accessibility can amplify discrimination in generative AI
Carolin Holtermann, Minh Duc Bui, Kaitlyn Zhou +3
Hundreds of millions of people rely on large language models (LLMs) for education, work, and even healthcare. Yet these models are known to reproduce and amplify social biases pres…
Different Demographic Cues Yield Inconsistent Conclusions About LLM Personalization and Bias
Manuel Tonneau, Neil K. R. Sehgal, Niyati Malhotra +7
Demographic cue-based evaluation is widely used to study how large language models (LLMs) adapt their responses to signaled demographic attributes within and across groups. This ap…
Large Language Models Discriminate Against Speakers of German Dialects
Minh Duc Bui, Carolin Holtermann, Valentin Hofmann +2
Dialects represent a significant component of human culture and are found across all regions of the world. In Germany, more than 40% of the population speaks a regional dialect (Ad…
Aligned but Blind: Alignment Increases Implicit Bias by Reducing Awareness of Race
Lihao Sun, Chengzhi Mao, Valentin Hofmann +1
Although value-aligned language models (LMs) appear unbiased in explicit bias evaluations, they often exhibit stereotypes in implicit word association tasks, raising concerns about…
IssueBench: Millions of Realistic Prompts for Measuring Issue Bias in LLM Writing Assistance
Paul Röttger, Musashi Hinck, Valentin Hofmann +4
Large language models (LLMs) are helping millions of users write texts about diverse issues, and in doing so expose users to different ideas and perspectives. This creates concerns…
Dialect prejudice predicts AI decisions about people's character, employability, and criminality
Valentin Hofmann, Pratyusha Ria Kalluri, Dan Jurafsky +1
Hundreds of millions of people now interact with language models, with uses ranging from serving as a writing aid to informing hiring decisions. Yet these language models are known…