DefinePK

Abstract

Global machine learning (ML) supply chains are increasingly complex, integrating diverse stakeholders across data providers, model developers, cloud service vendors, regulators, and end-users. Ensuring transparency, accountability, and efficiency within these interconnected systems requires effective stakeholder engagement frameworks paired with robust performance metric reporting models. This paper reviews emerging approaches to stakeholder collaboration in global ML supply chains, emphasizing participatory governance, cross-border policy alignment, and industry–academia partnerships. It also evaluates the role of performance metrics in measuring operational resilience, ethical compliance, energy efficiency, data integrity, and equity of outcomes. By synthesizing case studies from multinational ML deployments, the paper highlights gaps in standardized reporting practices and identifies risks of misaligned incentives, regulatory fragmentation, and opacity in algorithmic accountability. Furthermore, it explores the integration of digital technologies—such as blockchain-based audit trails and real-time dashboards—to strengthen transparency and trust among stakeholders. The study proposes a conceptual model that aligns stakeholder priorities with quantifiable reporting standards to optimize both performance and trust in ML supply chains. Ultimately, this review advances an interdisciplinary understanding of how engagement and reporting mechanisms can drive sustainable, resilient, and ethically grounded global ML ecosystems.
Keywords: Machine Learning Supply Chains, Stakeholder Engagement, Performance Metrics, Transparency and Accountability, Ethical AI Governance, Global Supply Chain Resilience.