Responsible AI is paramount in the realm of machine learning, especially with the growing call for AI regulation. This series addresses the practical needs of organizations and AI developers, offering hands-on learning to identify, debug, and mitigate issues associated with AI solutions. By leveraging both LLMs and traditional ML models, you’ll gain valuable insights to navigate the complexities of building AI solutions responsibly.

In this session, you’ll learn how to debug and mitigate an ML model issue using error analysis, data analysis, model explainability, model performance, and fairness assessment.

Learn more about responsible AI principles and practices at aka.ms/Responsible-AIPrinciples

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