New and Advanced Explainable Artificial Intelligence Approaches for Managing Project Risks

Project Risk Management (PRM) is a critical component of modern project governance, ensuring that uncertainties are systematically identified, assessed, and mitigated to minimize their impact on project outcomes. However, existing PRM practices remain largely subjective and qualitative, relying on expert judgment and static checklists that fail to capture dynamic interdependencies and evolving risk interactions. Recent advances in Artificial Intelligence (AI), particularly Machine Learning (ML) have improved predictive accuracy in PRM, but the opacity of many high-performing models has hindered adoption in safety-critical and managerial contexts where transparency, traceability, and stakeholder trust are essential. This thesis addresses these challenges by developing a suite of Explainable Artificial Intelligence (XAI) frameworks to support trustworthy project risk analysis. XAI has emerged as a promising approach to enhance transparency and interpretability in AI-based risk analysis; however, existing methods still exhibit several critical limitations that motivate this research. Current XAI techniques struggle to capture interrelations and cascading effects among risk factors, often producing fragmented explanations that overlook the structural dependencies underlying overall project vulnerability. In PRM, this limitation is particularly critical as risks are inherently interdependent, and disturbances in one aspect can propagate across multiple dimensions of project performance. Without incorporating such relationships into XAI models, the underlying causes of risk cannot be effectively identified, leading to explanations that lack depth and meaningful insight. Moreover, many post-hoc XAI models exhibit inconsistent or unstable explanations due to random neighborhood sampling or sensitivity to input variation, leading to unreliable outputs that are difficult to reproduce. In addition, existing XAI approaches remain largely static, offering one-way descriptions that overlook the temporal evolution and causal propagation of risks. Finally, they lack mechanisms for interactive engagement, preventing decision-makers from dynamically querying model behavior or exploring alternative “what-if” scenarios through conversational interfaces. Collectively, these limitations underscore the need for an integrated XAI-driven framework that unifies interpretability, scalability, causality, and interactivity. To address these shortcomings, this thesis defines four interrelated research objectives. Each objective focuses on a specific methodological limitation, namely interpretability, scalability, causality, and interactivity, while collectively forming a cohesive, progressive framework for explainable, trustworthy project risk management. The research commences by addressing the lack of interpretability and structured reasoning in existing risk models through the Interpretable Risk Assessment Framework based on Belief Rule-Based systems (IRAF-BRB). Traditional AI models for risk prediction often sacrifice interpretability for performance, making their outputs difficult for decision-makers to trust. IRAF-BRB resolves this issue by integrating expert knowledge with data-driven learning under explicit interpretability constraints. It ensures that model parameters remain meaningful, consistent with domain expertise, and explainable in linguistic terms. By optimizing belief rules using a modified Differential Evolution strategy while preserving human-interpretable reasoning, IRAF-BRB achieves both transparency and predictive reliability. This framework establishes the foundation for interpretable AI in PRM by showing that high accuracy can coexist with auditable, expert-aligned rule-based reasoning. While the IRAF-BRB ensures interpretability, it remains limited in scalability and adaptability to high-dimensional data. To overcome these limitations, the second framework, the Multi-Module Explainable Artificial Intelligence for Project Risk Management (MMXAI-PRM), extends explainability to complex, data-rich environments. MMXAI-PRM integrates generative modeling and post-hoc explanation to handle nonlinear relationships and class imbalance that traditional interpretable models cannot address. By combining synthetic data generation and local explanation, it enables consistent, stable interpretive reasoning even when training data are limited or heterogeneous. The framework provides richer explanatory insight by identifying key risk interactions and quantifying their relative influence, thereby enabling more informed prioritization and mitigation strategies. This contribution advances scalability and generalizability in explainable risk modeling, bridging the gap between interpretable logic-based systems and high-capacity learning models. Despite these improvements, earlier frameworks remain limited to static state-based representations that do not explicitly model the temporal evolution or causal propagation of risks.