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Extended Empirical Validation of the Explainability Solution Space

arXiv 2603.01235 ↗First posted 1 Mar 2026cs.AI cs.SEcited by 0

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Abstract (arXiv)

This technical report provides an extended validation of the Explainability Solution Space (ESS) through cross-domain evaluation. While initial validation focused on employee attrition prediction, this study introduces a heterogeneous intelligent urban resource allocation system to demonstrate the generality and domain-independence of the ESS framework. The second case study integrates tabular, temporal, and geospatial data under multi-stakeholder governance conditions. Explicit quantitative positioning of representative XAI families is provided for both contexts. Results confirm that ESS rankings are not domain-specific but adapt systematically to governance roles, risk profiles, and stakeholder configurations. The findings reinforce ESS as a generalizable operational decision-support instrument for explainable AI strategy design across socio-technical systems.

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References · 5

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
  4. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,416
  5. 2019Explaining Machine Learning Classifiers through Diverse Counterfactual Explanationscited by 614

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