Understanding surrogate explanations: the interplay between complexity, fidelity and coverage
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Abstract (arXiv)
This paper analyses the fundamental ingredients behind surrogate explanations to provide a better understanding of their inner workings. We start our exposition by considering global surrogates, describing the trade-off between complexity of the surrogate and fidelity to the black-box being modelled. We show that transitioning from global to local - reducing coverage - allows for more favourable conditions on the Pareto frontier of fidelity-complexity of a surrogate. We discuss the interplay between complexity, fidelity and coverage, and consider how different user needs can lead to problem formulations where these are either constraints or penalties. We also present experiments that demonstrate how the local surrogate interpretability procedure can be made interactive and lead to better explanations.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 6 of its 12 references that are in the map. First posted on arXiv 9 Jul 2021.
Builds on
6 of 12 references.
References · 6
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
- 2018Local Rule-Based Explanations of Black Box Decision Systemscited by 165
- 2018Defining Locality for Surrogates in Post-hoc Interpretablitycited by 33
- 2019bLIMEy: Surrogate Prediction Explanations Beyond LIMEcited by 17
- 2021On the overlooked issue of defining explanation objectives for local-surrogate explainerscited by 5
- 2021Explainers in the Wild: Making Surrogate Explainers Robust to Distortions through Perceptioncited by 4
Built on by
Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 0; 2025: 0; 2026: 1.
4 papers.
- 2021Uncertainty Quantification of Surrogate Explanations: an Ordinal Consensus Approachcited by 6
- 2021Attention-like feature explanation for tabular datacited by 3
- 2026Neuron-Anchored Rule Extraction for Large Language Models via Contrastive Hierarchical Ablationcited by 2
- 2023Interpretability from a new lens: Integrating Stratification and Domain knowledge for Biomedical Applicationscited by 0
- 2026Neuron-Anchored Rule Extraction for Large Language Models via Contrastive Hierarchical Ablation4 May 2026
- 2023Interpretability from a new lens: Integrating Stratification and Domain knowledge for Biomedical Applications15 Mar 2023
- 2021Uncertainty Quantification of Surrogate Explanations: an Ordinal Consensus Approach17 Nov 2021
- 2021Attention-like feature explanation for tabular data10 Aug 2021