# Contrastive Explanations with Local Foil Trees

arXiv: https://arxiv.org/abs/1806.07470 · HTML: https://mlsanity.com/paper/1806.07470/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2018.

## Built on by (most cited first, 30 of 34)

- [If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques](https://mlsanity.com/paper/2103.01035/) (2021)
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- [Towards Relatable Explainable AI with the Perceptual Process](https://mlsanity.com/paper/2112.14005/) (2021)
- [Human-Centered Concept Explanations for Neural Networks](https://mlsanity.com/paper/2202.12451/) (2022)
- [On the Computation of Necessary and Sufficient Explanations](https://mlsanity.com/paper/2203.10451/) (2022)
- [Logic for Explainable AI](https://mlsanity.com/paper/2305.05172/) (2023)
- [Concept Gradient: Concept-based Interpretation Without Linear Assumption](https://mlsanity.com/paper/2208.14966/) (2022)
- [From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence](https://mlsanity.com/paper/2104.13299/) (2021)
- [SurvNAM: The machine learning survival model explanation](https://mlsanity.com/paper/2104.08903/) (2021)
- [Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills](https://mlsanity.com/paper/2410.04253/) (2024)
- [Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence](https://mlsanity.com/paper/2112.14466/) (2021)
- [On the Horizon: Interactive and Compositional Deepfakes](https://mlsanity.com/paper/2209.01714/) (2022)
- [MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation](https://mlsanity.com/paper/2205.15540/) (2022)
- [On Quantitative Evaluations of Counterfactuals](https://mlsanity.com/paper/2111.00177/) (2021)
- [Large Language Models are Interpretable Learners](https://mlsanity.com/paper/2406.17224/) (2024)
- [A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures](https://mlsanity.com/paper/2404.00673/) (2024)
- [A Review of Explainable Artificial Intelligence in Manufacturing](https://mlsanity.com/paper/2107.02295/) (2021)
- [Contrastive Explanations for Argumentation-Based Conclusions](https://mlsanity.com/paper/2107.03265/) (2021)
- [Policy Optimization with Sparse Global Contrastive Explanations](https://mlsanity.com/paper/2207.06269/) (2022)
- [Semantic XAI for contextualized demand forecasting explanations](https://mlsanity.com/paper/2104.00452/) (2021)
- [Enriching Artificial Intelligence Explanations with Knowledge Fragments](https://mlsanity.com/paper/2204.05579/) (2022)
- [(When) Are Contrastive Explanations of Reinforcement Learning Helpful?](https://mlsanity.com/paper/2211.07719/) (2022)
- [SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis](https://mlsanity.com/paper/2108.02572/) (2021)
- [VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search](https://mlsanity.com/paper/2108.02725/) (2021)
- [Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation](https://mlsanity.com/paper/2109.05778/) (2021)
- [Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors](https://mlsanity.com/paper/2202.02830/) (2022)
- [Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2203.11011/) (2022)
- [Explanation-Guided Fairness Testing through Genetic Algorithm](https://mlsanity.com/paper/2205.08335/) (2022)
- [Using Graph Neural Networks for Program Termination](https://mlsanity.com/paper/2207.14648/) (2022)

## Abstract (arXiv)

Recent advances in interpretable Machine Learning (iML) and eXplainable AI (XAI) construct explanations based on the importance of features in classification tasks. However, in a high-dimensional feature space this approach may become unfeasible without restraining the set of important features. We propose to utilize the human tendency to ask questions like "Why this output (the fact) instead of that output (the foil)?" to reduce the number of features to those that play a main role in the asked contrast. Our proposed method utilizes locally trained one-versus-all decision trees to identify the disjoint set of rules that causes the tree to classify data points as the foil and not as the fact. In this study we illustrate this approach on three benchmark classification tasks.