lstate/explainability-in-practice

code for the paper "Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in Senegal" # Explainability in Practice Code for the paper "Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in Senegal" (L. State, H. Salat, S. Rubrichi and Z. Smoreda) *Version 2 (newer version)*: Archival at The first World Conference on eXplainable AI (XAI 2023) Updated code (and paper). *Version 1 (older version)*: Non-archival at TSRML Workshop (NeurIPS 2022) Jupyter notebook files: 1) training the classifiers 2) generating the explanations for LIME and SHAP separately, 3 different notebooks as LIME generation is separated from plotting You can find the paper here