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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.01235 |
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| _version_ | 1866912950487351296 |
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| author | Mestre, Antoni Albert, Manoli Gil, Miriam Pelechano, Vicente |
| author_facet | Mestre, Antoni Albert, Manoli Gil, Miriam Pelechano, Vicente |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01235 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Extended Empirical Validation of the Explainability Solution Space Mestre, Antoni Albert, Manoli Gil, Miriam Pelechano, Vicente Artificial Intelligence Software Engineering 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. |
| title | Extended Empirical Validation of the Explainability Solution Space |
| topic | Artificial Intelligence Software Engineering |
| url | https://arxiv.org/abs/2603.01235 |