Michael Hind is a distinguished research staff member in the IBM Research AI department in Yorktown Heights, New York. His current research passion is the area of trusted AI, focusing on governance, transparency, explainability, and fairness of AI systems. He has authored over 50 publications, served on over 50 program committees, and has given several keynotes and invited talks at top universities, conferences, and government settings. Hind has led dozens of researchers to successfully transfer technology to various parts of IBM and helped launch several successful open source projects, such as AI Fairness 360 and AI Explainability 360. His 2000 paper on adaptive optimization was recognized as the OOPSLA’00 Most Influential Paper and his work on Jikes RVM was recognized with the SIGPLAN Software Award in 2012. Hind is an ACM Distinguished Scientist, and a member of IBM’s Academy of Technology.