Image credit: Jason LucasAbstract
I presented work at the 2023 CRA-WP Workshop detecting COVID-19 false claims in the linguistically diverse, low-resource setting of the Caribbean islands. This project analyzed how computational solutions trained solely on English data fail to capture the nuances around misinformation propagation in developing regions. Our experiments found neural networks and even powerful transformer architectures struggle when tested on non-English claims translated from Caribbean languages. My talk discussed insights around the difficulty of transferring AI models across low-resource languages and vastly different information ecosystems. I also highlighted ideas for enhancing cross-lingual understanding and inclusion in misinformation detection systems deployed in at-risk communities.
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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.