mlsanityData to 14 Oct 2026

Understanding XAI Through the Philosopher's Lens: A Historical Perspective

arXiv 2407.18782 ↗First posted 26 Jul 2024cs.AIcited by 2

Not on the map. Why?

Abstract (arXiv)

Despite explainable AI (XAI) has recently become a hot topic and several different approaches have been developed, there is still a widespread belief that it lacks a convincing unifying foundation. On the other hand, over the past centuries, the very concept of explanation has been the subject of extensive philosophical analysis in an attempt to address the fundamental question of "why" in the context of scientific law. However, this discussion has rarely been connected with XAI. This paper tries to fill in this gap and aims to explore the concept of explanation in AI through an epistemological lens. By comparing the historical development of both the philosophy of science and AI, an intriguing picture emerges. Specifically, we show that a gradual progression has independently occurred in both domains from logical-deductive to statistical models of explanation, thereby experiencing in both cases a paradigm shift from deterministic to nondeterministic and probabilistic causality. Interestingly, we also notice that similar concepts have independently emerged in both realms such as, for example, the relation between explanation and understanding and the importance of pragmatic factors. Our study aims to be the first step towards understanding the philosophical underpinnings of the notion of explanation in AI, and we hope that our findings will shed some fresh light on the elusive nature of XAI.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 12 of its 54 references that are in the map. First posted on arXiv 26 Jul 2024.

Builds on

12 of 54 references.

References · 12

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,416
  4. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,206
  5. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,089
  6. 2018A Survey Of Methods For Explaining Black Box Modelscited by 973
  7. 2018Metrics for Explainable AI: Challenges and Prospectscited by 260
  8. 2021On Explaining Random Forests with SATcited by 57
  9. 2020The Pragmatic Turn in Explainable Artificial Intelligence (XAI)cited by 54
  10. 2021Trading Complexity for Sparsity in Random Forest Explanationscited by 32
  11. 2021Principles of Explanation in Human-AI Systemscited by 25
  12. 2023Explainability is NOT a Gamecited by 23

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.