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 this 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.

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Request a demo: https://bit.ly/3QDwzDq

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