Resilience management framework for complex systems using Explainable Artificial Intelligence

In volatile environments, traditional decision-making frameworks face challenges as they depend on cognitive patterns that may not be universally applicable. Identifying situational dependencies and interpreting disruption subtleties and their cadence is crucial for improving predictive robustness. This position paper examines the importance of cross-sector interdependencies in building resilience within complex systems. The paper emphasizes the role of Explainable Artificial Intelligence (XAI) in enhancing resilience strategies by augmenting human intuition and decision-making. By incorporating transparency and interpretability into AI systems, XAI fosters trust and usability—essential for making impactful, cost-effective, and timely decisions. The increasing frequency of supply chain disruptions accentuates the necessity to design systems that facilitate effective monitoring and proactive planning for early identification and mitigation of risks. To address these challenges, the paper proposes a synergistic model that leverages AI-powered decision support to enhance cross-sector horizon scanning. This model complements expert judgment and human instinct and empowers the development of resilience strategies that are interpretable, justifiable, traceable, and accessible to an array of actors across disciplines. Furthermore, the paper advocates for interdisciplinary collaboration as essential for optimizing resilience strategies, emphasizing the integration of diverse perspectives and knowledge systems to create adaptive and effective responses to complex systemic risks.