Explainable Artificial Intelligence (XAI) for Project Governance: Improving Transparency and Stakeholder Trust in Automated Project Decision

Aim: This paper examines the role of Explainable Artificial Intelligence (XAI) in strengthening project governance through improved transparency, accountability, and stakeholder engagement. It also explores how explainability can enhance trust in AI-driven project decisions and support informed oversight in project management. Methods: The study draws on existing literature in AI governance, project management, and explainable machine learning. It examines the growing adoption of AI-driven systems in project management and analyzes the role of Explainable Artificial Intelligence (XAI) in addressing governance challenges associated with the use of advanced machine learning models. Results: The findings indicate that XAI contributes to improved project governance by enabling decision traceability, facilitating human–AI collaboration, and reducing concerns related to bias and uncertainty. However, the study also identifies significant implementation challenges, including organizational readiness, implementation complexity, and the trade-off between model performance and interpretability. Conclusion: The study concludes that Explainable Artificial Intelligence has the potential to strengthen project governance by improving transparency, accountability, and stakeholder confidence in AI-generated decisions. Nevertheless, successful implementation requires addressing the practical challenges associated with deploying explainable AI systems in project environments. Recommendations: The study recommends further research on human-centered explainability, AI governance frameworks, and trust measurement mechanisms for AI-enabled project environments to enhance the effective governance and adoption of Explainable Artificial Intelligence in project management.