# Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation

43 Pages Posted: 12 Jun 2019 Last revised: 20 Jul 2022

See all articles by Cynthia Rudin

## Cynthia Rudin

Duke University - Pratt School of Engineering; Duke University

## Yaron Shaposhnik

University of Rochester - Simon Business School

Date Written: May 28, 2019

### Abstract

We develop a method for understanding specific predictions made by (global) predictive models by constructing (local) models tailored to each specific observation (these are also called explanations" in the literature). Unlike existing work that explains'' specific observations by \textit{approximating} global models in the vicinity of these observations, we fit models that are \textit{globally-consistent} with predictions made by the global model on past data. We focus on rule-based models (also known as association rules or conjunctions of predicates), which are interpretable and widely used in practice. We design multiple algorithms to extract such rules from discrete and continuous datasets, and study their theoretical properties. Finally, we apply these algorithms to multiple credit-risk models trained on real-world data from FICO and demonstrate that our approach effectively produces sparse summary-explanations of these models in a short period of time. Our approach is model-agnostic (that is, can be used to explain any predictive model), and solves a minimum set cover problem to construct its summaries.

Keywords: Explainable Artificial Intelligence (XAI), Local Explanations, Interpretability, Credit Risk

JEL Classification: C4, C19

Suggested Citation

Rudin, Cynthia and Rudin, Cynthia and Shaposhnik, Yaron, Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation (May 28, 2019). Available at SSRN: https://ssrn.com/abstract=3395422 or http://dx.doi.org/10.2139/ssrn.3395422