A Data Science Playbook for Explainable ML/AI

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Presented by

Domino Chief Data Scientist Josh Poduska, and VP of Marketing Jon Rooney

About this talk

Navigating Predictive and Interpretable Models Model ethics, interpretability, and trust will be seminal issues in data science in the coming decade. This technical webinar discusses traditional and modern approaches for interpreting black box models. Additionally, we will review cutting edge research coming out of UCSF, CMU, and industry. This new research reveals holes in traditional approaches like SHAP and LIME when applied to some deep net architectures and introduces a new approach to xML/xAI where interpretability is a hyperparameter in the model building phase rather than a post-modeling exercise. We will provide step-by-step guides that practitioners can use in their work to navigate this interesting space. We will review code examples of interpretability techniques. You can follow along with the presentation by running your own notebook hosted in Domino's trial environment. Create a free trial account at: http://dominodatalab.com/try?utm_source=brighttalk

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