@article{01JP9VGEQHQWRRQ03HENH0GK7N,
  abstract     = {{The remarkable advancements in machine learning (ML) have led to its extensive adoption in Project Risk
Management (PRM), leveraging its powerful predictive capabilities and data-driven insights that support
proactive decision-making. Nevertheless, the ‘‘black-box" nature of ML models obscures the reasoning behind
predictions, undermining transparency and trust. To address this, existing explainable artificial intelligence
(XAI) techniques, such as Local Interpretable Model-agnostic Explanations (LIME), Global Priors-based LIME
(G-LIME), and SHapley Additive exPlanations (SHAP), have been applied to interpret black-box models. Yet,
they face considerable limitations in PRM, including their inability to model cascading effects and multi-level
dependencies among risk factors, suffering from inconsistencies due to random sampling, and failure to capture
non-linear interactions in high-dimensional risk data. In response to these shortcomings, this paper proposes
the Multi-Module eXplainable Artificial Intelligence framework for Project Risk Management (MMXAI-PRM), a
novel approach designed to address the unique demands of PRM. The framework consists of three modules: the
Risk Relationship Insight Module (RRIM), which models risk dependencies using a Knowledge Graph (KG); the
Risk Factor Influence Analysis Module (RFIAM), which introduces a Conditional Tabular Generative Adversarial
Network-aided Local Interpretable Model-agnostic Explanations using Kernel Ridge Regression (CTGAN-LIMEKR) to ensure explanation consistency and handle non-linearity; and the Visualization and Interpretation
Module (VIM), which synthesizes these insights into an interpretable, chain-based representation. Extensive
experiments demonstrate that MMXAI-PRM delivers more consistent, stable, and accurate explanations than
existing XAI methods. By improving interpretability, it enhances trust in AI-driven risk predictions and equips
project managers with actionable insights, advancing decision-making in PRM.}},
  articleno    = {{110427}},
  author       = {{Badhon, Bodrunnessa and Chakrabortty, Ripon K. and Anavatti, Sreenatha G. and Vanhoucke, Mario}},
  issn         = {{0952-1976}},
  journal      = {{ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE}},
  keywords     = {{Explainable Artificial Intelligence,Knowledge Graph,Conditional Tabular Generative Adversarial,Networks,Local Interpretable Model-Agnostic,Explanations,Project Risk Management}},
  language     = {{eng}},
  pages        = {{20}},
  title        = {{A multi-module explainable artificial intelligence framework for project risk management : enhancing transparency in decision-making}},
  url          = {{http://doi.org/10.1016/j.engappai.2025.110427}},
  volume       = {{148}},
  year         = {{2025}},
}

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