A Data Science Playbook for explainable ML/xAI
This technical webinar on Aug 14 discusses traditional and modern approaches for interpreting black box models. Additionally, we will review cutting edge research coming out of UCSF, CMU, and industry.
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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 explainable ML/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 here.
We hope to see you there! Register to attend, or to receive the webinar materials here.
