Holistic Explainable AI (H-XAI): Extending Transparency Beyond Developers in AI-Driven Decision Making

• URL: http://arxiv.org/abs/2508.05792v1
• Date: Thu, 07 Aug 2025 19:06:08 GMT
• Title: Holistic Explainable AI (H-XAI): Extending Transparency Beyond Developers in AI-Driven Decision Making
• Authors: Kausik Lakkaraju, Siva Likitha Valluru, Biplav Srivastava,
• Abstract summary: We introduce Holistic-XAI (H-XAI), a unified framework that integrates causal rating methods with traditional XAI methods to support explanation as an interactive, multi-method process.<n>H-XAI allows stakeholders to ask a series of questions, test hypotheses, and compare model behavior against automatically constructed random and biased baselines.<n>It combines instance-level and global explanations, adapting to each stakeholder's goals, whether understanding individual decisions, assessing group-level bias, or evaluating robustness under perturbations.
• Score: 4.852258514542496
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Current eXplainable AI (XAI) methods largely serve developers, often focusing on justifying model outputs rather than supporting diverse stakeholder needs. A recent shift toward Evaluative AI reframes explanation as a tool for hypothesis testing, but still focuses primarily on operational organizations. We introduce Holistic-XAI (H-XAI), a unified framework that integrates causal rating methods with traditional XAI methods to support explanation as an interactive, multi-method process. H-XAI allows stakeholders to ask a series of questions, test hypotheses, and compare model behavior against automatically constructed random and biased baselines. It combines instance-level and global explanations, adapting to each stakeholder's goals, whether understanding individual decisions, assessing group-level bias, or evaluating robustness under perturbations. We demonstrate the generality of our approach through two case studies spanning six scenarios: binary credit risk classification and financial time-series forecasting. H-XAI fills critical gaps left by existing XAI methods by combining causal ratings and post-hoc explanations to answer stakeholder-specific questions at both the individual decision level and the overall model level.

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