#Openbox - The Explainable AI Conundrum with Agus Sudjianto Part 1

OPENBOX aims at bringing an easier understanding of open problems that helps in finding solutions for such problems. For the said purpose, I interview researchers and practitioners who have published works on open problems in a variety of areas of Artificial Intelligence and Machine Learning to collect a simplified understanding of these open problems. These are published as podcast series.  My name is Sundar. I am an Ethics and Risk professional and an AI Ethics researcher. I am the host of this podcast.  Ideas emerge when curiosity meets clarity. Here we go with OPENBOX to bring clarity to those curious minds looking to solve real-world problems.  This project is done in collaboration with ForHumanity. ForHumanity is a 501(c)(3) nonprofit organization dedicated to minimizing the downside risks of AI and autonomous systems. ForHumanity develops criteria for an independent audit of AI systems. To know more visit https://forhumanity.center/.  Agus Sudjianto is Senior VP, Risk and Technology for Enterprise. In his most recent role Agus was an executive vice president and head of Model Risk for Wells Fargo, where he is responsible for enterprise model risk management and serving as Chair of the Model Risk Committee. He is also Executive in Residence of School of Data Science University of North Carolina in Charlotte. He has several patents to his credit, numerous contributions to research and a great group of friends and followers in the community. We are going to be pondering with him about 2 key things today: explainability and AI risk, both of which he has dealt with at multitudes over several years. In the first part of the podcast we deal with the explainability issues in current AI/ ML pipeline.