joshpoduska / xai-tech-talk

Explainable AI

Model interpretability is a seminal topic in the field of data science. The field is moving quickly. Practitioners can leverage a range of tools to provide explainability to their models from traditional approaches to the latests in deep neural network explainers.

Project Contents

This project contains starter code for a few of these approaches and some educational material on xAI.

• SHAP_and_LIME.ipynb - a how-to notebook
• rtemis.R - a how-to R file
• traditional_methods.ipynb - a how-to notebook
• Navigating Interpretable and Predictive Models.pdf - slides from the tech talk on xAI which includes many links to further research

Suggested Actions

• Explore the SHAP_and_LIME.ipynb notebook

Reference Material

• Learn more about xAI by browsing the instructional pdf
• updaetd project files can be found at https://github.com/dominodatalab/domino-reference-project-xai

Prerequisites

This project uses standard python libraries and any base python install should work well. The Python libraries shap, lime, and pycebox will probably need to be installed separately.

There are several additional R libraries needed to run rtemis. This library changes frequently, sometimes breaking dependencies. See the R scipt included in this project for details.