# Defining Locality for Surrogates in Post-hoc Interpretablity

arXiv: https://arxiv.org/abs/1806.07498 · HTML: https://mlsanity.com/paper/1806.07498/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2018.

## Built on by (most cited first, 30 of 33)

- [Towards Faithful Model Explanation in NLP: A Survey](https://mlsanity.com/paper/2209.11326/) (2022)
- [Evaluating Robustness of Counterfactual Explanations](https://mlsanity.com/paper/2103.02354/) (2021)
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- [RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Model](https://mlsanity.com/paper/2206.00700/) (2022)
- [Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation](https://mlsanity.com/paper/2310.03112/) (2023)
- [Which LIME should I trust? Concepts, Challenges, and Solutions](https://mlsanity.com/paper/2503.24365/) (2025)
- [SLISEMAP: Supervised dimensionality reduction through local explanations](https://mlsanity.com/paper/2201.04455/) (2022)
- [Understanding Prediction Discrepancies in Machine Learning Classifiers](https://mlsanity.com/paper/2104.05467/) (2021)
- [Understanding surrogate explanations: the interplay between complexity, fidelity and coverage](https://mlsanity.com/paper/2107.04309/) (2021)
- [Why do explanations fail? A typology and discussion on failures in XAI](https://mlsanity.com/paper/2405.13474/) (2024)
- [On the overlooked issue of defining explanation objectives for local-surrogate explainers](https://mlsanity.com/paper/2106.05810/) (2021)
- [XPROAX-Local explanations for text classification with progressive neighborhood approximation](https://mlsanity.com/paper/2109.15004/) (2021)
- [Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?](https://mlsanity.com/paper/2112.13210/) (2021)
- [Model Transparency and Interpretability : Survey and Application to the Insurance Industry](https://mlsanity.com/paper/2209.00562/) (2022)
- [Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE](https://mlsanity.com/paper/2509.03626/) (2025)
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- [Towards responsible AI for education: Hybrid human-AI to confront the Elephant in the room](https://mlsanity.com/paper/2504.16148/) (2025)
- [Latent Debate: A Surrogate Framework for Interpreting LLM Thinking](https://mlsanity.com/paper/2512.01909/) (2025)
- [ExpProof : Operationalizing Explanations for Confidential Models with ZKPs](https://mlsanity.com/paper/2502.03773/) (2025)
- [TrustyAI Explainability Toolkit](https://mlsanity.com/paper/2104.12717/) (2021)
- [Explaining text classifiers through progressive neighborhood approximation with realistic samples](https://mlsanity.com/paper/2302.07733/) (2023)
- [Transparent Neighborhood Approximation for Text Classifier Explanation](https://mlsanity.com/paper/2411.16251/) (2024)
- [Surrogate Modeling for Explainable Predictive Time Series Corrections](https://mlsanity.com/paper/2412.19897/) (2024)
- [Explanations Go Linear: Post-hoc Explainability for Tabular Data with Interpretable Meta-Encoding](https://mlsanity.com/paper/2504.20667/) (2025)
- [ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI](https://mlsanity.com/paper/2607.09649/) (2026)
- [Fairness of Machine Learning Algorithms in Demography](https://mlsanity.com/paper/2202.01013/) (2022)
- [Uncertainty in Additive Feature Attribution methods](https://mlsanity.com/paper/2311.17446/) (2023)
- [Enhanced Photonic Chip Design via Interpretable Machine Learning Techniques](https://mlsanity.com/paper/2505.09266/) (2025)

## Abstract (arXiv)

Local surrogate models, to approximate the local decision boundary of a black-box classifier, constitute one approach to generate explanations for the rationale behind an individual prediction made by the back-box. This paper highlights the importance of defining the right locality, the neighborhood on which a local surrogate is trained, in order to approximate accurately the local black-box decision boundary. Unfortunately, as shown in this paper, this issue is not only a parameter or sampling distribution challenge and has a major impact on the relevance and quality of the approximation of the local black-box decision boundary and thus on the meaning and accuracy of the generated explanation. To overcome the identified problems, quantified with an adapted measure and procedure, we propose to generate surrogate-based explanations for individual predictions based on a sampling centered on particular place of the decision boundary, relevant for the prediction to be explained, rather than on the prediction itself as it is classically done. We evaluate the novel approach compared to state-of-the-art methods and a straightforward improvement thereof on four UCI datasets.