They also use the insights into the model for debugging, validating model behavior matches their objectives, and to check for bias. In this section we briefly explain how you can explain why your model made the predictions it did with the various interpretability packages of the Azure Machine Learning Python SDK.ĭuring the training phase of the development cycle, model designers and evaluators can use interpretability output of a model to verify hypotheses and build trust with stakeholders. Or do you want to know why the prediction was made and possibly pay for the interpretability with a drop in predictive performance? When it comes to predictive modeling, you have to make a trade-off: do you just want to know what is predicted? For example, the probability that a customer will churn or how effective some drug will be for a patient. In the next few paragraphs, we summarize the most important aspects presented during our seminars: Specifically, we shared a few insights and practical demos on how to make machine learning models interpretable and accessible with Azure Machine Learning. On September 9th, 10th and 11th, 2019, we organized a few seminars at the University of California, Berkeley around the topic of Ethics in AI.

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