# Why model why? Assessing the strengths and limitations of LIME

arXiv: https://arxiv.org/abs/2012.00093 · HTML: https://mlsanity.com/paper/2012.00093/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 21 of 21)

- [Which LIME should I trust? Concepts, Challenges, and Solutions](https://mlsanity.com/paper/2503.24365/) (2025)
- [A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI](https://mlsanity.com/paper/2302.05284/) (2023)
- [Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?](https://mlsanity.com/paper/2112.13210/) (2021)
- [Exploiting auto-encoders and segmentation methods for middle-level explanations of image classification systems](https://mlsanity.com/paper/2106.05037/) (2021)
- [Local Interpretable Model Agnostic Shap Explanations for machine learning models](https://mlsanity.com/paper/2210.04533/) (2022)
- [A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series](https://mlsanity.com/paper/2405.03234/) (2024)
- [A survey and taxonomy of methods interpreting random forest models](https://mlsanity.com/paper/2407.12759/) (2024)
- [Retention Is All You Need](https://mlsanity.com/paper/2304.03103/) (2023)
- [A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network Security](https://mlsanity.com/paper/2501.07801/) (2025)
- [Coherent Local Explanations for Mathematical Optimization](https://mlsanity.com/paper/2502.04840/) (2025)
- [Context-Aware Visualization for Explainable AI Recommendations in Social Media: A Vision for User-Aligned Explanations](https://mlsanity.com/paper/2508.00674/) (2025)
- [Relational Local Explanations](https://mlsanity.com/paper/2212.12374/) (2022)
- [Balancing Explainability-Accuracy of Complex Models](https://mlsanity.com/paper/2305.14098/) (2023)
- [What's meant by explainable model: A Scoping Review](https://mlsanity.com/paper/2307.09673/) (2023)
- [Consensus of state of the art mortality prediction models: From all-cause mortality to sudden death prediction](https://mlsanity.com/paper/2308.16067/) (2023)
- [Advancing Explainable AI Toward Human-Like Intelligence: Forging the Path to Artificial Brain](https://mlsanity.com/paper/2402.06673/) (2024)
- [Explainable AI Methods for Multi-Omics Analysis: A Survey](https://mlsanity.com/paper/2410.11910/) (2024)
- [From Abstract to Actionable: Pairwise Shapley Values for Explainable AI](https://mlsanity.com/paper/2502.12525/) (2025)
- [Owen-based Semantics and Hierarchy-Aware Explanation (O-Shap)](https://mlsanity.com/paper/2602.17107/) (2026)
- [Enhancing LIME using Neural Decision Trees](https://mlsanity.com/paper/2603.20919/) (2026)
- [CRAFT: Fine-Tuning Pre-hoc Explainability in AI-native 6G RAN](https://mlsanity.com/paper/2609.00590/) (2026)

## Abstract (arXiv)

When it comes to complex machine learning models, commonly referred to as black boxes, understanding the underlying decision making process is crucial for domains such as healthcare and financial services, and also when it is used in connection with safety critical systems such as autonomous vehicles. As such interest in explainable artificial intelligence (xAI) tools and techniques has increased in recent years. However, the effectiveness of existing xAI frameworks, especially concerning algorithms that work with data as opposed to images, is still an open research question. In order to address this gap, in this paper we examine the effectiveness of the Local Interpretable Model-Agnostic Explanations (LIME) xAI framework, one of the most popular model agnostic frameworks found in the literature, with a specific focus on its performance in terms of making tabular models more interpretable. In particular, we apply several state of the art machine learning algorithms on a tabular dataset, and demonstrate how LIME can be used to supplement conventional performance assessment methods. In addition, we evaluate the understandability of the output produced by LIME both via a usability study, involving participants who are not familiar with LIME, and its overall usability via an assessment framework, which is derived from the International Organisation for Standardisation 9241-11:1998 standard.