AI Explained: Machine Learning for High Risk Applications
July 19, 2023
9AM PT / 12PM ET
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Although AI is being widely adopted, it poses several adversarial risks that can be harmful to organizations and users. Parul Pandey, Principal Data Scientist at H2O.ai and co-author of Machine Learning for High-Risk Applications will explore how data scientists and ML practitioners can improve AI outcomes with proper model risk management techniques.
Watch AI Explained to learn:
- Technical approaches for explainability, model validation, bias management, and ML security
- Key principles of model risk management during ML/LLM implementation
- How to prevent negative AI outcomes in production, such as abuses, attacks, and failures
AI Explained is our AMA series featuring experts on the most pressing issues facing AI and ML teams.
Featured Speakers
Parul Pandey
Principal Data Scientist
at
H2O.ai
Parul Pandey has a background in Electrical Engineering and currently works as a Principal Data Scientist at H2O.ai. Prior to this, she was working as a Machine Learning Engineer at Weights & Biases. She is also a Kaggle Grandmaster in the notebooks category and was one of Linkedin’s Top Voices in the Software Development category in 2019. Parul has written multiple articles focused on Data Science and Software development for various publications and mentors, speaks, and delivers workshops on topics related to Responsible AI. She is currently part of the “The 2023 Kaggle AI Report” as an area chair and section editor, focusing on the section dedicated to the theme of continued studies of AI ethics.
Krishnaram Kenthapadi
Chief AI Officer & Scientist
at
Fiddler AI
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).
