Adaptive Explainable Artificial Intelligence for Governance-Oriented Risk Scoring in Organizational Change Management

Abstract

Explainable Artificial Intelligence has progressively emerged as a foundational pillar in the governance of complex socio-technical systems, especially where algorithmic recommendations influence strategic organizational outcomes. Among the most sensitive of such environments is change management, in which decisions about system modifications, software updates, infrastructure reconfiguration, and operational process reengineering carry both financial and institutional risk. Contemporary organizations increasingly rely on Change Advisory Boards to evaluate, approve, or reject proposed changes, yet these decisions are now often informed by predictive models that estimate implementation risk. While such models promise improved accuracy and consistency, they also introduce epistemic opacity that can undermine trust, accountability, and regulatory compliance. This tension between predictive power and interpretability constitutes one of the most pressing challenges of modern AI-driven governance.

This article develops a comprehensive theoretical and methodological framework for integrating explainable artificial intelligence into predictive risk scoring for Change Advisory Board decision processes. Anchored in the emerging literature on algorithmic governance and model interpretability, the study positions explainable models not merely as technical artifacts but as institutional instruments that mediate between human judgment, regulatory requirements, and organizational legitimacy. Particular attention is given to recent advances in predictive risk scoring for change management, where machine learning models assess variables such as change scope, historical failure rates, interdependency structures, and operational volatility to generate probabilistic risk evaluations that guide CAB deliberations, as exemplified in the work of Varanasi (2025).

Drawing on a wide corpus of research on explainable artificial intelligence, interpretability metrics, counterfactual reasoning, feature attribution, and user trust, this article articulates how explanation methods transform opaque predictions into actionable and contestable knowledge. Through an extended conceptual methodology and a literature-grounded interpretive results section, the study demonstrates that explainable risk scoring enhances not only transparency but also procedural justice, stakeholder confidence, and long-term system resilience. The analysis further shows that explanation frameworks such as SHAP, rule ensembles, counterfactual profiles, and ceteris paribus plots can be aligned with governance principles in order to convert algorithmic outputs into decision-relevant narratives that Change Advisory Boards can evaluate, challenge, and refine.

The discussion situates explainable risk scoring within broader debates on algorithmic accountability, socio-technical trust, and the future of decision-support systems in organizational governance. It argues that without explainability, predictive risk systems risk becoming technocratic instruments that displace rather than support human judgment, whereas with well-designed explanatory mechanisms they can function as epistemic partners in collective decision making. By synthesizing engineering, management, and information governance perspectives, this article advances a model of explainable AI as an essential component of ethically and operationally sustainable change management.