Michael Hind is a Distinguished Research Leader in the IBM Research AI department. His current research passion is on Trusted AI, focusing on governance, transparency, explainability, and fairness of AI systems. Michael has launched several popular open source projects, such as AI Fairness 360 and AI Explainability 360, and has successfully transferred technology into several IBM products, such as AI FactSheets. He has given several invited talks at top universities, conferences, and government settings and is working closely with IBM customers to understand their needs. Michael has received the SIGPLAN Software Award, is an ACM Distinguished Scientist, and a member of IBM's Academy of Technology.