# The Pragmatic Turn in Explainable Artificial Intelligence (XAI)

arXiv: https://arxiv.org/abs/2002.09595 · HTML: https://mlsanity.com/paper/2002.09595/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2020.

## Built on by (most cited first, 30 of 54)

- [Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions](https://mlsanity.com/paper/2310.19775/) (2023)
- [Human-Centered Explainable AI (XAI): From Algorithms to User Experiences](https://mlsanity.com/paper/2110.10790/) (2021)
- [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)
- [A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts](https://mlsanity.com/paper/2105.07190/) (2021)
- [A Survey on Interpretable Reinforcement Learning](https://mlsanity.com/paper/2112.13112/) (2021)
- [Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications](https://mlsanity.com/paper/2103.04244/) (2021)
- [The Who in XAI: How AI Background Shapes Perceptions of AI Explanations](https://mlsanity.com/paper/2107.13509/) (2021)
- [Investigating Explainability of Generative AI for Code through Scenario-based Design](https://mlsanity.com/paper/2202.04903/) (2022)
- [Explainability Pitfalls: Beyond Dark Patterns in Explainable AI](https://mlsanity.com/paper/2109.12480/) (2021)
- [Seamful XAI: Operationalizing Seamful Design in Explainable AI](https://mlsanity.com/paper/2211.06753/) (2022)
- [Interpretable Machine Learning in Physics: A Review](https://mlsanity.com/paper/2503.23616/) (2025)
- [Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence](https://mlsanity.com/paper/2112.14466/) (2021)
- [Being Right for Whose Right Reasons?](https://mlsanity.com/paper/2306.00639/) (2023)
- [The Dead Salmons of AI Interpretability](https://mlsanity.com/paper/2512.18792/) (2025)
- [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 Means-End Account of Explainable Artificial Intelligence](https://mlsanity.com/paper/2208.04638/) (2022)
- [AI, Opacity, and Personal Autonomy](https://mlsanity.com/paper/2210.08995/) (2022)
- [A New Perspective on Evaluation Methods for Explainable Artificial Intelligence (XAI)](https://mlsanity.com/paper/2307.14246/) (2023)
- [Explanatory Pluralism in Explainable AI](https://mlsanity.com/paper/2106.13976/) (2021)
- [Explanation as a process: user-centric construction of multi-level and multi-modal explanations](https://mlsanity.com/paper/2110.03759/) (2021)
- [Towards Human-Interpretable Prototypes for Visual Assessment of Image Classification Models](https://mlsanity.com/paper/2211.12173/) (2022)
- [Forms of Understanding for XAI-Explanations](https://mlsanity.com/paper/2311.08760/) (2023)
- [False Sense of Security in Explainable Artificial Intelligence (XAI)](https://mlsanity.com/paper/2405.03820/) (2024)
- [Information That Matters: Exploring Information Needs of People Affected by Algorithmic Decisions](https://mlsanity.com/paper/2401.13324/) (2024)
- [Machine Learning and Theory Ladenness -- A Phenomenological Account](https://mlsanity.com/paper/2409.11277/) (2024)
- [A Mechanistic Explanatory Strategy for XAI](https://mlsanity.com/paper/2411.01332/) (2024)
- [ContextualSHAP : Enhancing SHAP Explanations Through Contextual Language Generation](https://mlsanity.com/paper/2512.07178/) (2025)
- [Towards Human Cognition Level-based Experiment Design for Counterfactual Explanations (XAI)](https://mlsanity.com/paper/2211.00103/) (2022)
- [Human-centered trust framework: An HCI perspective](https://mlsanity.com/paper/2305.03306/) (2023)
- [Axe the X in XAI: A Plea for Understandable AI](https://mlsanity.com/paper/2403.00315/) (2024)

## Abstract (arXiv)

In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will lack a well-defined goal. Aside from providing a clearer objective for XAI, focusing on understanding also allows us to relax the factivity condition on explanation, which is impossible to fulfill in many machine learning models, and to focus instead on the pragmatic conditions that determine the best fit between a model and the methods and devices deployed to understand it. After an examination of the different types of understanding discussed in the philosophical and psychological literature, I conclude that interpretative or approximation models not only provide the best way to achieve the objectual understanding of a machine learning model, but are also a necessary condition to achieve post-hoc interpretability. This conclusion is partly based on the shortcomings of the purely functionalist approach to post-hoc interpretability that seems to be predominant in most recent literature.