The Longtail Impact of Generative AI on Disinformation: Harmonizing Dichotomous Perspectives

Sep 1, 2024·
Jason Lucas
Jason Lucas
,
Barani Maung Maung
,
Maryam Tabar
,
Keegan McBride
,
Dongwon Lee
· 1 min read
Abstract
Generative AI (GenAI) poses significant risks in creating convincing yet factually ungrounded content, particularly in “longtail” contexts of high-impact events and resource-limited settings. While some argue that current disinformation ecosystems naturally limit GenAI’s impact, we contend that this perspective neglects longtail contexts where disinformation consequences are most profound. This article analyzes the potential impact of GenAI’s disinformation in longtail events and settings, focusing on 1) quantity: its ability to flood information ecosystems during critical events; 2) quality: the challenge of distinguishing authentic content from high-quality GenAI content; 3) personalization: its capacity for precise microtargeting exploiting individual vulnerabilities; and 4) hallucination: the danger of unintentional false information generation, especially in high-stakes situations. We then propose strategies to combat disinformation in these contexts. Our analysis underscores the need for proactive measures to mitigate risks, safeguard social unity, and combat the erosion of trust in the GenAI era, particularly in vulnerable communities and during critical events.
Type
Publication
In IEEE Intelligent Systems
publication
Note
Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.
Note
This work provides a comprehensive analysis of Generative AI’s impact on disinformation in critical contexts where traditional mitigation strategies may be insufficient.

Add the publication’s full text or supplementary notes here. You can use rich formatting such as including code, math, and images.

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.