Generative AI Disproportionately Harms Long Tail Users

Nov 1, 2024·
Barani Maung Maung
,
Keegan McBride
Jason Lucas
Jason Lucas
,
Maryam Tabar
,
Dongwon Lee
· 1 min read
Abstract
Generative AI (GenAI) poses significant risks in creating convincing yet factually ungrounded content, particularly affecting “longtail” users who may have limited resources or technical expertise to identify and combat disinformation. This article examines how GenAI disproportionately impacts these vulnerable user groups, analyzing the structural inequalities that emerge when advanced AI systems are deployed without adequate consideration for diverse user populations and their varying capabilities to navigate AI-generated content.
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In Computer
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This work examines how Generative AI systems disproportionately impact longtail users, highlighting critical equity concerns in AI deployment.

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Jason Lucas
Authors
Ph.D. Candidate in Informatics

I am a PhD candidate in Informatics in the College of IST at Penn State University, where I conduct research at the PIKE Research Lab under the guidance of Dr. Dongwon Lee. I specialize in AI/ML research focused on Information Integrity, Safe and Ethical AI, including combating harmful content across multiple languages and modalities. My research spans low-resource multilingual NLP, generative AI, and adversarial machine learning, with work extending across 79 languages. I have published 12 papers with 260+ citations in premier venues including ACL, EMNLP, IEEE, and NAACL.

My doctoral research focuses on bridging the digital language divide through transfer learning, classification (NLU), generation (NLG), adversarial attacks, and developing end-to-end AI pipelines using RAG and Agentic AI workflows for combating multilingual threats. Drawing from my Grenadian background and knowledge of local Creole languages, I bring a global perspective to AI challenges, working to democratize state-of-the-art AI capabilities for underserved linguistic communities worldwide. My mission is to develop robust multilingual multimodal systems and mitigate evolving security vulnerabilities while enhancing access to human language technology through cutting-edge solutions.

As an NSF LinDiv Fellow, I conduct transdisciplinary research advancing human-AI language interaction for social good. I actively mentor 5+ research interns and teach Applied Generative AI courses. Through industry experience at Lawrence Livermore National Lab, Interaction LLC, and Coalfire, I bridge academic research with practical applications in combating evolving security threats and enhancing global AI accessibility. I see multilingual advances and interdisciplinary collaboration as a competitive advantage, not a communication challenge. Beyond research, I stay active through dance, fitness, martial arts, and community service.