# Developing Guidelines for Functionally-Grounded Evaluation of Explainable Artificial Intelligence using Tabular Data

arXiv: https://arxiv.org/abs/2410.12803 · HTML: https://mlsanity.com/paper/2410.12803/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 86 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Builds on (most cited first, 24 of 24)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency](https://mlsanity.com/paper/2001.09734/) (2020)
- [Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications](https://mlsanity.com/paper/2103.04244/) (2021)
- [ALIME: Autoencoder Based Approach for Local Interpretability](https://mlsanity.com/paper/1909.02437/) (2019)
- [The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations](https://mlsanity.com/paper/2205.03295/) (2022)
- [Model-agnostic Feature Importance and Effects with Dependent Features -- A Conditional Subgroup Approach](https://mlsanity.com/paper/2006.04628/) (2020)
- [Toward Explainable AI for Regression Models](https://mlsanity.com/paper/2112.11407/) (2021)
- [TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models](https://mlsanity.com/paper/2109.08438/) (2021)
- [To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods](https://mlsanity.com/paper/2106.00461/) (2021)
- [Post-hoc explanation of black-box classifiers using confident itemsets](https://mlsanity.com/paper/2005.01992/) (2020)
- [Select Wisely and Explain: Active Learning and Probabilistic Local Post-hoc Explainability](https://mlsanity.com/paper/2108.06907/) (2021)
- [Conclusive Local Interpretation Rules for Random Forests](https://mlsanity.com/paper/2104.06040/) (2021)
- [s-LIME: Reconciling Locality and Fidelity in Linear Explanations](https://mlsanity.com/paper/2208.01510/) (2022)
- [SLISEMAP: Supervised dimensionality reduction through local explanations](https://mlsanity.com/paper/2201.04455/) (2022)
- [A general-purpose method for applying Explainable AI for Anomaly Detection](https://mlsanity.com/paper/2207.11564/) (2022)
- [Altruist: Argumentative Explanations through Local Interpretations of Predictive Models](https://mlsanity.com/paper/2010.07650/) (2020)
- [Coalitional strategies for efficient individual prediction explanation](https://mlsanity.com/paper/2104.00765/) (2021)
- [Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space](https://mlsanity.com/paper/1907.13525/) (2019)
- [Local Multi-Label Explanations for Random Forest](https://mlsanity.com/paper/2207.01994/) (2022)
- [Using Decision Tree as Local Interpretable Model in Autoencoder-based LIME](https://mlsanity.com/paper/2204.03321/) (2022)

## Built on by (most cited first, 2 of 2)

- [Let the Trial Begin: A Mock-Court Approach to Vulnerability Detection using LLM-Based Agents](https://mlsanity.com/paper/2505.10961/) (2025)
- [Transparent AI: The Case for Interpretability and Explainability](https://mlsanity.com/paper/2507.23535/) (2025)

## Abstract (arXiv)

Explainable Artificial Intelligence (XAI) techniques are used to provide transparency to complex, opaque predictive models. However, these techniques are often designed for image and text data, and it is unclear how fit-for-purpose they are when applied to tabular data. As XAI techniques are rarely evaluated in settings with tabular data, the applicability of existing evaluation criteria and methods are also unclear and needs (re-)examination. For example, some works suggest that evaluation methods may unduly influence the evaluation results when using tabular data. This lack of clarity on evaluation procedures can lead to reduced transparency and ineffective use of XAI techniques in real world settings. In this study, we examine literature on XAI evaluation to derive guidelines on functionally-grounded assessment of local, post hoc XAI techniques. We identify 20 evaluation criteria and associated evaluation methods, and derive guidelines on when and how each criterion should be evaluated. We also identify key research gaps to be addressed by future work. Our study contributes to the body of knowledge on XAI evaluation through in-depth examination of functionally-grounded XAI evaluation protocols, and has laid the groundwork for future research on XAI evaluation.