A Comprehensive Perspective on Explainable AI across the Machine Learning Workflow

TL;DR

This paper introduces HXAI, a user-centric, comprehensive framework that integrates explanation throughout the entire machine learning workflow, addressing gaps in current tools and enhancing transparency and trust in AI systems.

Contribution

It presents a novel taxonomy and survey of explanation needs, grounded in interdisciplinary theories, and demonstrates how large-language models can facilitate stakeholder-specific explanations.

Findings

Identifies critical gaps in existing explainability tools.

Develops a comprehensive taxonomy for explanation components.

Shows how AI agents with large-language models can improve explanations.

Abstract

Artificial intelligence is reshaping science and industry, yet many users still regard its models as opaque "black boxes". Conventional explainable artificial-intelligence methods clarify individual predictions but overlook the upstream decisions and downstream quality checks that determine whether insights can be trusted. In this work, we present Holistic Explainable Artificial Intelligence (HXAI), a user-centric framework that embeds explanation into every stage of the data-analysis workflow and tailors those explanations to users. HXAI unifies six components (data, analysis set-up, learning process, model output, model quality, communication channel) into a single taxonomy and aligns each component with the needs of domain experts, data analysts and data scientists. A 112-item question bank covers these needs; our survey of contemporary tools highlights critical coverage gaps. Grounded in theories of human explanation, principles from human-computer interaction and findings from empirical user studies, HXAI identifies the characteristics that make explanations clear, actionable and cognitively manageable. A comprehensive taxonomy operationalises these insights, reducing terminological ambiguity and enabling rigorous coverage analysis of existing toolchains. We further demonstrate how AI agents that embed large-language models can orchestrate diverse explanation techniques, translating technical artifacts into stakeholder-specific narratives that bridge the gap between AI developers and domain experts. Departing from traditional surveys or perspective articles, this work melds concepts from multiple disciplines, lessons from real-world projects and a critical synthesis of the literature to advance a novel, end-to-end viewpoint on transparency, trustworthiness and responsible AI deployment.