Abstract: Artificial Intelligence (AI) and machine learning, alongside the advances in decision making, prediction, knowledge extraction, and logic reasoning are widely implemented to address challenges in diverse areas, for example, machine translation, fraud detection, content recommendation, clinical diagnosis, and autonomous devices. In this talk, we will present and discuss some explorations on adopting explainable AI for mental health and social good. The talk will start with a recent work on quantitative evaluation of factors effecting citizens' happiness with interpretability, and be concluded with a study on depression detection using machine learning techniques based on Quality of Life scales. These typical works reflect our current endeavour on human-centred AI - AI by humanity, for humanity, and with consideration of humanity.
Bio: Dr. Xiaohui Tao is Senior Member of IEEE and ACM, an active researcher in AI, and Associate Professor (Computing) in School of Mathematics, Physics and Computing. His research interests include natural language processing, data analytics, knowledge engineering, information retrieval, and health informatics. His research results have been published on 150+ papers including many top-tier journals and conferences. He was awarded "Research Performance Award" and "Discipline Research Performance Improvement Award" by SoMPC in 2021, and received ARC DP grant (Ref. DP220101360) in 2021 and Australian Endeavour Research Fellowship in 2015.
For more information, please contact Enamul Kabir.