Extended Empirical Validation of the Explainability Solution Space
This work provides an incremental validation of a framework for explainable AI strategy design, addressing the problem of domain-specific limitations in XAI for decision-makers in socio-technical systems.
The authors extended validation of the Explainability Solution Space (ESS) by applying it to a heterogeneous urban resource allocation system, confirming that ESS rankings adapt systematically to governance roles and stakeholder configurations, reinforcing its generalizability across socio-technical systems.
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.