- Murdoch University, Australia
Organized by
Augmus Pty Ltd and School of Information Technology at Murdoch University
Abstract
Generative AI (GenAI) has transformed research workflows across disciplines, but it also raises critical challenges related to fairness, transparency, privacy, and ethical use. This workshop introduces researchers and practitioners to the principles and practices of Responsible AI in the GenAI era. Participants will explore common challenges, such as hallucinations in LLM outputs, dataset and model biases, and privacy risks, using real examples from recent studies. The workshop emphasizes practical, research-oriented practices, such as human-in-the-loop validation, reproducible experiment design, and comprehensive documentation. Attendees will also gain hands-on exposure to tools and methods for explainability, bias analysis, monitoring, and reproducibility, demonstrating how to integrate Responsible AI practices into their research workflows. By the end of the session, participants will leave with actionable strategies to conduct GenAI research responsibly.
Biography
Dr Sirui Li is a Lecturer at the School of Information Technology, Murdoch University, and Co-Associate Dean for Equity, Diversity and Inclusion (EDI). Her research expertise spans artificial intelligence, natural language understanding, knowledge graphs, responsible AI, and multimodal models. She focuses on applying these technologies to real-world problems across interdisciplinary domains, including healthcare, agriculture, and mining.
Her work has been published in leading venues including Artificial Intelligence Review, IEEE Transactions on Affective Computing, ACL, EMNLP, WWW, and ICSME. She serves as Chapter Chair for the IEEE Computational Intelligence Society and the IEEE Robotics and Automation Society (Western Australia Section). She is also actively involved in organising and reviewing for multiple international conferences.
Sponsor
Supported by IEEE Systems, Man and Cybernetics (SMC) Society Transforming Educational Assets and Materials (TEAM) Program