AI Explained: A Data Scientist’s Guide to Explainable AI
November 15, 2022
10 AM PT / 1 PM ET
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Organizations are increasingly investing in explainable AI (XAI) to provide context and visibility into model behavior, bias, and predictions. By understanding the “how” and “why” behind models, data scientists and ML teams can optimize model performance and accelerate model deployment.
Watch this AI Explained to learn:
- The value of XAI for ML models
- How to do XAI the right way
- Why explainability is critical during experimentation and training
AI Explained is our new AMA series featuring experts on the most pressing issues facing AI and ML teams.
Sr. Director, AI/ML Platform
Rhombus Power Inc
Chief AI Officer & Scientist
Prior to Fiddler, he was a Principal Scientist at Amazon AWS AI and LinkedIn AI, where he led the fairness, explainability, privacy, and model understanding initiatives. Krishnaram received his Ph.D. in Computer Science from Stanford University in 2006. He serves regularly on the program committees of KDD, WWW, WSDM, and related conferences. His work has been recognized through awards at NAACL, WWW, SODA, CIKM, ICML AutoML workshop, and Microsoft’s AI/ML conference (MLADS). He has published 50+ papers, with 4500+ citations and filed 150+ patents (70 granted).