Image credit: Limeng CuiAbstract
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.
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Jason Lucas
Ph.D. Student in Informatics
My research interests include low-resource multilingual NLP, linguistics, adversarial machine learning and mis/disinformation generation/detection. My Ph.D. thesis is in the area of applying artificial intelligence for cybersecurity and social good, with a focus on low-resource multilingual natural language processing. More specifically, I develop NLP techniques to promote cybersecurity, combat mis/disinformation, and enable AI accessibility for non-English languages and underserved populations. This involves creating novel models and techniques for tasks like multilingual and crosslingual text classification, machine translation, text generation, and adversarial attacks in limited training data settings. My goal is to democratize state-of-the-art AI capabilities by extending them beyond high-resource languages like English into the long tail of lower-resourced languages worldwide. By innovating robust learning approaches from scarce linguistic data, this research aims to open promising directions where AI can have dual benefits strengthening security, integrity and social welfare across diverse global locales.