Presented at the 6th Pan African Proffessional Alliance Conference Proceedings

May 9, 2022·
Jason Lucas
Jason Lucas
· 1 min read
Image credit: Limeng Cui
Abstract
At the 2023 Panapa Professional Alliance Conference, I presented one of my research on combating the spread of fake news in low-resource and multilingual settings. As false information increasingly moves across languages, AI-powered fake news detectors trained solely on high-resource languages like English struggle with generalizability. I discussed a case study focused on COVID-19 misinformation circulating in Caribbean countries to showcase this challenge. Through my presentation, I made the case for developing cross-lingual fake news detection models capable of serving users across languages. My talk covered issues around longtail knowledge gaps, translation inadequacies, and the need for representative multilingual data. At the time of this Talk, I was a 2nd-Year PhD student at Penn State working on the frontiers of natural language processing, cybersecurity and cross-lingual understanding. My work unveils the challenges and solutions in using AI and machine learning to identify fake news in multilingual contexts, underscoring the need for more inclusive and effective technological approaches in this ever-evolving field.
Location

Penn State University

University Park, Pennsylvania

event
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Jason Lucas
Authors
Jason Lucas
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.