Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation
By Cynthia Rudin, Yaron Shaposhnik. 2019
42 citations recorded by the source corpus.
We develop a method for interpreting 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 approximating global models in the vicinity of these observations, we fit models that are 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 short period of time. Our approach is model-agnostic (that is, can be used to interpret any predictive model), and solves a minimum set cover problem to construct its summaries.
Subfields: Artificial Intelligence, Safety, Robustness & Interpretability, Information Extraction
How researchers see this paper
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In Artificial Intelligence
- Lesia Semenova
- Chudi Zhong
- Zhi Chen
- Haiyang Huang
- Cynthia Rudin
- Tong Wang
- Octavio Loyola-González
- Berk Ustun
In Safety, Robustness & Interpretability
- Lesia Semenova
- Zhi Chen
- Chudi Zhong
- Haiyang Huang
- Cynthia Rudin
- Octavio Loyola-González
- Tong Wang
- S. Lo Piano
In Information Extraction
Citations in the corpus
- Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “Some works optimize for individual rules (Dash et al., 2018a; Rudin and Shaposhnik, 2019) Over the last few years, great progress has been made on optimizing the combination of accuracy and sparsity for logical models, but there are still many challenges that need to be solved.”