T. Waschulzik

A Configurable Framework for Quantifying and Comparing Interpretability Across ML Models and Methods

This paper introduces a configurable framework of quantitative metrics to evaluate and rank interpretability across diverse ML models and interpretability techniques, enabling practitioners to systematically compare interpretability across a wide range of algorithms and techniques.