Название: Explainable AI and Blockchain for Secure and Agile Supply Chains: Enhancing Transparency, Traceability, and AccountabilityАвтор: Ajay Kumar Sharma, Narasimha Rao Vajjhala, Rakshit Kothari, Rajasekhara Mouly Potluri
Издательство: CRC Press
Год: 2025
Страниц: 236
Язык: английский
Формат: epub (true)
Размер: 10.1 MB
This book examines the transformative potential of integrating Explainable Artificial Intelligence (XAI) and blockchain technology in modern supply chain management.
It explores how these innovative technologies address pressing challenges such as data transparency, traceability, fraud prevention, and operational efficiency in complex global supply chains. With detailed analyses, case studies, and real-world applications, these chapters provide insights into leveraging XAI to demystify AI decision-making and blockchain, ensuring data security and decentralized accountability. Key topics include sustainable practices, smart contract automation, and human-centric approaches to enhancing trust and collaboration among stakeholders.
Explainable AI (XAI) emerged as a solution to address these concerns by enabling AI systems to articulate the rationale behind their outputs in ways that are understandable to human users. Transparency in AI is not just about satisfying curiosity; it is about ensuring accountability, mitigating biases, and fostering trust in automated systems. For instance, in supply chains, where AI might predict demand trends or optimize delivery routes, the ability to explain how decisions were made can help businesses understand and refine their strategies, avoid errors, and ensure regulatory compliance. Explainability becomes crucial when dealing with sensitive issues such as ethical sourcing or consumer data privacy, where decisions must be auditable and justifiable. The key characteristics of XAI—interpretability, transparency, and accountability—define its transformative potential. Interpretability refers to the ability of human stakeholders to understand the outputs of an AI model. This can involve presenting simplified visualizations, intuitive explanations, or even human-like reasoning that makes complex decision processes accessible. Transparency goes a step further by revealing the underlying logic or data relationships used by the AI system. It ensures that the inner workings of the AI are not concealed, enabling users to assess the reliability and fairness of the outcomes. Accountability, the third pillar of XAI, ensures that AI decisions are verifiable, with clear records of the processes involved. This is critical in supply chains, where decisions often have legal, ethical, and financial implications. Explainability also enhances collaborative decision-making between humans and AI. In traditional supply chains, where multiple stakeholders must work together, having a clear understanding of AI-driven insights can facilitate consensus and coordinated action. Moreover, XAI allows businesses to identify and rectify biases in their AI systems. By exposing patterns and decision pathways, XAI can help organizations ensure fairness and inclusivity, avoiding outcomes that could inadvertently favor one group over another. As a result, XAI not only demystifies AI systems but also strengthens their utility in diverse and complex applications, such as managing global supply chains.
This comprehensive volume serves as a valuable resource for academics, industry leaders, and policymakers seeking to harness cutting-edge technologies for building resilient and transparent supply chains.
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