# bLIMEy: Surrogate Prediction Explanations Beyond LIME

arXiv: https://arxiv.org/abs/1910.13016 · HTML: https://mlsanity.com/paper/1910.13016/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2019.

## Built on by (most cited first, 17 of 17)

- [Datamodels: Predicting Predictions from Training Data](https://mlsanity.com/paper/2202.00622/) (2022)
- [ContextCite: Attributing Model Generation to Context](https://mlsanity.com/paper/2409.00729/) (2024)
- [Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence](https://mlsanity.com/paper/2112.14466/) (2021)
- [Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse](https://mlsanity.com/paper/2410.22598/) (2024)
- [Which LIME should I trust? Concepts, Challenges, and Solutions](https://mlsanity.com/paper/2503.24365/) (2025)
- [Understanding surrogate explanations: the interplay between complexity, fidelity and coverage](https://mlsanity.com/paper/2107.04309/) (2021)
- [Uncertainty Quantification of Surrogate Explanations: an Ordinal Consensus Approach](https://mlsanity.com/paper/2111.09121/) (2021)
- [What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks](https://mlsanity.com/paper/2403.12730/) (2024)
- [A Review of Explainable Artificial Intelligence in Manufacturing](https://mlsanity.com/paper/2107.02295/) (2021)
- [Explainers in the Wild: Making Surrogate Explainers Robust to Distortions through Perception](https://mlsanity.com/paper/2102.10951/) (2021)
- [What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components](https://mlsanity.com/paper/2209.03813/) (2022)
- [Local Interpretable Model Agnostic Shap Explanations for machine learning models](https://mlsanity.com/paper/2210.04533/) (2022)
- [SurvBeX: An explanation method of the machine learning survival models based on the Beran estimator](https://mlsanity.com/paper/2308.03730/) (2023)
- [Integrating Prior Knowledge in Post-hoc Explanations](https://mlsanity.com/paper/2204.11634/) (2022)
- [Identification, explanation and clinical evaluation of hospital patient subtypes](https://mlsanity.com/paper/2301.08019/) (2023)
- [Sampling Based On Natural Image Statistics Improves Local Surrogate Explainers](https://mlsanity.com/paper/2208.03961/) (2022)
- [CHILLI: A data context-aware perturbation method for XAI](https://mlsanity.com/paper/2407.07521/) (2024)

## Abstract (arXiv)

Surrogate explainers of black-box machine learning predictions are of paramount importance in the field of eXplainable Artificial Intelligence since they can be applied to any type of data (images, text and tabular), are model-agnostic and are post-hoc (i.e., can be retrofitted). The Local Interpretable Model-agnostic Explanations (LIME) algorithm is often mistakenly unified with a more general framework of surrogate explainers, which may lead to a belief that it is the solution to surrogate explainability. In this paper we empower the community to "build LIME yourself" (bLIMEy) by proposing a principled algorithmic framework for building custom local surrogate explainers of black-box model predictions, including LIME itself. To this end, we demonstrate how to decompose the surrogate explainers family into algorithmically independent and interoperable modules and discuss the influence of these component choices on the functional capabilities of the resulting explainer, using the example of LIME.