# False Sense of Security in Explainable Artificial Intelligence (XAI)

arXiv: https://arxiv.org/abs/2405.03820 · HTML: https://mlsanity.com/paper/2405.03820/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 6 May 2024.

## Builds on (most cited first, 16 of 16)

- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [Explanations can be manipulated and geometry is to blame](https://mlsanity.com/paper/1906.07983/) (2019)
- [Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities](https://mlsanity.com/paper/2111.06420/) (2021)
- [How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations](https://mlsanity.com/paper/2101.08758/) (2021)
- [The Pragmatic Turn in Explainable Artificial Intelligence (XAI)](https://mlsanity.com/paper/2002.09595/) (2020)
- [Adversarial attacks and defenses in explainable artificial intelligence: A survey](https://mlsanity.com/paper/2306.06123/) (2023)
- [From "AI" to Probabilistic Automation: How Does Anthropomorphization of Technical Systems Descriptions Influence Trust?](https://mlsanity.com/paper/2404.16047/) (2024)
- [Feature Perturbation Augmentation for Reliable Evaluation of Importance Estimators in Neural Networks](https://mlsanity.com/paper/2303.01538/) (2023)
- [Fidelity of Interpretability Methods and Perturbation Artifacts in Neural Networks](https://mlsanity.com/paper/2203.02928/) (2022)
- [Trends in Explainable AI (XAI) Literature](https://mlsanity.com/paper/2301.05433/) (2023)
- [Human-Centered Evaluation of XAI Methods](https://mlsanity.com/paper/2310.07534/) (2023)

## Built on by (most cited first, 4 of 4)

- [Benchmarking Vision Transformers and CNNs for Thermal Photovoltaic Fault Detection with Explainable AI Validation](https://mlsanity.com/paper/2509.07039/) (2025)
- [Traccia: An OpenTelemetry-Based Governance Platform for AI Systems](https://mlsanity.com/paper/2607.14309/) (2026)
- [An Empirical Study and Assessment of EU AI Act Compliance Checkers](https://mlsanity.com/paper/2609.36228/) (2026)
- [For Those Who Believe in Faithfulness: Optimizing the Area Under Insertion and Deletion Curves for Ranking Relative Feature Importance](https://mlsanity.com/paper/2610.09844/) (2026)

## Abstract (arXiv)

A cautious interpretation of AI regulations and policy in the EU and the USA place explainability as a central deliverable of compliant AI systems. However, from a technical perspective, explainable AI (XAI) remains an elusive and complex target where even state of the art methods often reach erroneous, misleading, and incomplete explanations. "Explainability" has multiple meanings which are often used interchangeably, and there are an even greater number of XAI methods - none of which presents a clear edge. Indeed, there are multiple failure modes for each XAI method, which require application-specific development and continuous evaluation. In this paper, we analyze legislative and policy developments in the United States and the European Union, such as the Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, the AI Act, the AI Liability Directive, and the General Data Protection Regulation (GDPR) from a right to explanation perspective. We argue that these AI regulations and current market conditions threaten effective AI governance and safety because the objective of trustworthy, accountable, and transparent AI is intrinsically linked to the questionable ability of AI operators to provide meaningful explanations. Unless governments explicitly tackle the issue of explainability through clear legislative and policy statements that take into account technical realities, AI governance risks becoming a vacuous "box-ticking" exercise where scientific standards are replaced with legalistic thresholds, providing only a false sense of security in XAI.