# Not All Explanations are Created Equal: Investigating the Pitfalls of Current XAI Evaluation

arXiv: https://arxiv.org/abs/2511.03730 · HTML: https://mlsanity.com/paper/2511.03730/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 27 Sep 2025.

## Builds on (most cited first, 6 of 6)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research](https://mlsanity.com/paper/2102.07817/) (2021)
- [If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques](https://mlsanity.com/paper/2103.01035/) (2021)
- [Explainability Pitfalls: Beyond Dark Patterns in Explainable AI](https://mlsanity.com/paper/2109.12480/) (2021)
- [Explainability in Human-Agent Systems](https://mlsanity.com/paper/1904.08123/) (2019)
- [A Human-Grounded Evaluation Benchmark for Local Explanations of Machine Learning](https://mlsanity.com/paper/1801.05075/) (2018)

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

- [Quality Without Usefulness: LLM-Generated XAI Narratives as Trust Heuristics Rather Than Decision Aids](https://mlsanity.com/paper/2605.26770/) (2026)
- [Beyond Satisfaction: From Placebic to Actionable Explanations For Enhanced Understandability](https://mlsanity.com/paper/2512.06591/) (2025)

## Abstract (arXiv)

Explainable Artificial Intelligence (XAI) aims to create transparency in modern AI models by offering explanations of the models to human users. There are many ways in which researchers have attempted to evaluate the quality of these XAI models, such as user studies or proposed objective metrics like "fidelity". However, these current XAI evaluation techniques are ad hoc at best and not generalizable. Thus, most studies done within this field conduct simple user surveys to analyze the difference between no explanations and those generated by their proposed solution. We do not find this to provide adequate evidence that the explanations generated are of good quality since we believe any kind of explanation will be "better" in most metrics when compared to none at all. Thus, our study looks to highlight this pitfall: most explanations, regardless of quality or correctness, will increase user satisfaction. We also propose that emphasis should be placed on actionable explanations. We demonstrate the validity of both of our claims using an agent assistant to teach chess concepts to users. The results of this chapter will act as a call to action in the field of XAI for more comprehensive evaluation techniques for future research in order to prove explanation quality beyond user satisfaction. Additionally, we present an analysis of the scenarios in which placebic or actionable explanations would be most useful.