Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods

• URL: http://arxiv.org/abs/2607.14123v1
• Date: Thu, 18 Jun 2026 21:46:20 GMT
• Title: Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods
• Abstract summary: This paper argues that the machine learning community must pivot from ad-hoc XAI methods toward addressing foundational & structural challenges.<n>We conclude by outlining a practical checklist designed to shift XAI toward a more human-centered, action-oriented paradigm.
• Score: 56.14010017381489
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the proliferation of Explainable AI (XAI) techniques -- from feature attributions to sparse autoencoders -- explanations rarely influence real-world workflows. In practice, they are often generated and discarded without guiding meaningful action. This gap reflects foundational shortcomings: research has not yet established methodologies for integrating explanations into end-to-end, human-in-the-loop systems. This position paper argues that the machine learning community must pivot from ad-hoc XAI methods toward addressing foundational & structural challenges, including unclear problem formulations, underspecified evaluation objectives, and the absence of pipelines for explanation-driven feedback. We support this claim through an analysis of recent ICML, NeurIPS, and ICLR papers and a survey of XAI practitioners, revealing recurring issues that limit cumulative progress. We conclude by outlining a practical checklist designed to shift XAI toward a more human-centered, action-oriented paradigm. By emphasizing foundational clarity over the development of ad-hoc methods, we hope to provide a roadmap for integrating explanations into actionable, feedback-driven AI systems.

Related papers

• Emergent, not Immanent: A Baradian Reading of Explainable AI [37.51348424835944]
  We argue that interpretations emerge from situated entanglements of the AI model with humans, context, and the interpretative apparatus.<n>We propose design directions for XAI interfaces that support emergent interpretation.
  arXiv  Detail & Related papers  (2026-01-21T14:32:40Z)
• Towards Transparent AI: A Survey on Explainable Large Language Models [2.443957114877221]
  Large Language Models (LLMs) have played a pivotal role in advancing Artificial Intelligence (AI)<n>LLMs often struggle to explain their decision-making processes, making them a 'black box' and presenting a substantial challenge to explainability.<n>To overcome these limitations, researchers have developed various explainable artificial intelligence (XAI) methods.
  arXiv  Detail & Related papers  (2025-06-26T23:25:22Z)
• Chain of Methodologies: Scaling Test Time Computation without Training [77.85633949575046]
  Large Language Models (LLMs) often struggle with complex reasoning tasks due to insufficient in-depth insights in their training data.<n>This paper introduces the Chain of the (CoM) framework that enhances structured thinking by integrating human methodological insights.
  arXiv  Detail & Related papers  (2025-06-08T03:46:50Z)
• Demystifying Reinforcement Learning in Production Scheduling via Explainable AI [0.7515066610159392]
  Deep Reinforcement Learning (DRL) is a frequently employed technique to solve scheduling problems. Although DRL agents ace at delivering viable results in short computing times, their reasoning remains opaque. We apply two explainable AI (xAI) frameworks to describe the reasoning behind scheduling decisions of a specialized DRL agent in a flow production.
  arXiv  Detail & Related papers  (2024-08-19T09:39:01Z)
• Why do explanations fail? A typology and discussion on failures in XAI [5.366368559381279]
  We argue that the resulting harms arise from a complex overlap of multiple failures in XAI.<n>We propose a typological framework that helps revealing the nuanced complexities of explanation failures.
  arXiv  Detail & Related papers  (2024-05-22T09:32:24Z)
• Opening the Black-Box: A Systematic Review on Explainable AI in Remote Sensing [51.524108608250074]
  Black-box machine learning approaches have become a dominant modeling paradigm for knowledge extraction in remote sensing. We perform a systematic review to identify the key trends in the field and shed light on novel explainable AI approaches. We also give a detailed outlook on the challenges and promising research directions.
  arXiv  Detail & Related papers  (2024-02-21T13:19:58Z)
• Learning Transferable Conceptual Prototypes for Interpretable Unsupervised Domain Adaptation [79.22678026708134]
  In this paper, we propose an inherently interpretable method, named Transferable Prototype Learning ( TCPL) To achieve this goal, we design a hierarchically prototypical module that transfers categorical basic concepts from the source domain to the target domain and learns domain-shared prototypes for explaining the underlying reasoning process. Comprehensive experiments show that the proposed method can not only provide effective and intuitive explanations but also outperform previous state-of-the-arts.
  arXiv  Detail & Related papers  (2023-10-12T06:36:41Z)
• Re-Reading Improves Reasoning in Large Language Models [87.46256176508376]
  We introduce a simple, yet general and effective prompting method, Re2, to enhance the reasoning capabilities of off-the-shelf Large Language Models (LLMs) Unlike most thought-eliciting prompting methods, such as Chain-of-Thought (CoT), Re2 shifts the focus to the input by processing questions twice, thereby enhancing the understanding process. We evaluate Re2 on extensive reasoning benchmarks across 14 datasets, spanning 112 experiments, to validate its effectiveness and generality.
  arXiv  Detail & Related papers  (2023-09-12T14:36:23Z)
• Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
  Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome. Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations. We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
  arXiv  Detail & Related papers  (2021-06-14T20:48:48Z)
• A new interpretable unsupervised anomaly detection method based on residual explanation [47.187609203210705]
  We present RXP, a new interpretability method to deal with the limitations for AE-based AD in large-scale systems. It stands out for its implementation simplicity, low computational cost and deterministic behavior. In an experiment using data from a real heavy-haul railway line, the proposed method achieved superior performance compared to SHAP.
  arXiv  Detail & Related papers  (2021-03-14T15:35:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.