ODSC Webinar | Responsible AI: Debugging AI models for errors, fairness and explainability
Are the machine learning models we build really reliable? Traditional model performance techniques tend to have a limited view of a model's true accuracy. Errors in the model or data are common blind spots that can lead to inaccuracies or societal bias. In addition, the need to understand features driving a model’s outcome is becoming a necessity to meet some industry regulations for transparency and accountability.
This session will illustrate how to use model Error Analysis, Data Analysis, Explainability/Interpretability, Counterfactual/What-If, Casual analysis to debug and mitigate model issues faster. You will learn how to use Azure Machine Learning’s Responsible AI dashboard to analyze and identify potential model issues to help ML professionals produce AI solutions that are less harmful to society and more trustworthy.
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