Michael Hind is a Distinguished Research Staff Member at IBM Research, where he focuses on AI Governance and transparency. With his IBM colleagues, he has launched several popular open source projects, such as AI Fairness 360 and AI Explainability 360, and commercial offerings such as AI FactSheets. Michael works closely with customers to understand their needs and to accelerate the incorporation of research into IBM products. He has authored over 50 publications, served on over 50 program committees, and given several keynotes and invited talks at top universities, conferences, and government settings. His work on Trustworthy AI has been recognized with an IBM Corporate Award. Michael is an ACM Distinguished Scientist, and was elected to IBM's Academy of Technology.