# SMACE: A New Method for the Interpretability of Composite Decision Systems

arXiv: https://arxiv.org/abs/2111.08749 · HTML: https://mlsanity.com/paper/2111.08749/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 16 Nov 2021.

## Builds on (most cited first, 10 of 10)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods](https://mlsanity.com/paper/1911.02508/) (2019)
- [Problems with Shapley-value-based explanations as feature importance measures](https://mlsanity.com/paper/2002.11097/) (2020)
- [Explaining the Explainer: A First Theoretical Analysis of LIME](https://mlsanity.com/paper/2001.03447/) (2020)
- [Statistical stability indices for LIME: obtaining reliable explanations for Machine Learning models](https://mlsanity.com/paper/2001.11757/) (2020)
- [Looking Deeper into Tabular LIME](https://mlsanity.com/paper/2008.11092/) (2020)
- [AI Trust in business processes: The need for process-aware explanations](https://mlsanity.com/paper/2001.07537/) (2020)
- [Extending LIME for Business Process Automation](https://mlsanity.com/paper/2108.04371/) (2021)

## Built on by (most cited first, 2 of 2)

- [Faithful and Robust Local Interpretability for Textual Predictions](https://mlsanity.com/paper/2311.01605/) (2023)
- [EAMDrift: An interpretable self retrain model for time series](https://mlsanity.com/paper/2305.19837/) (2023)

## Abstract (arXiv)

Interpretability is a pressing issue for decision systems. Many post hoc methods have been proposed to explain the predictions of a single machine learning model. However, business processes and decision systems are rarely centered around a unique model. These systems combine multiple models that produce key predictions, and then apply decision rules to generate the final decision. To explain such decisions, we propose the Semi-Model-Agnostic Contextual Explainer (SMACE), a new interpretability method that combines a geometric approach for decision rules with existing interpretability methods for machine learning models to generate an intuitive feature ranking tailored to the end user. We show that established model-agnostic approaches produce poor results on tabular data in this setting, in particular giving the same importance to several features, whereas SMACE can rank them in a meaningful way.