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@article{188501,
        author = {Viraj Tathavadekar and Dr. Nitin R. Mahankale},
        title = {Ethical AI in Sustainable Supply Chain Learning: A Viewpoint on Trust, Transparency and Decent Work Transformation Across Global Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {3083-3087},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188501},
        abstract = {Purpose: This viewpoint examines the ethical imperatives surrounding artificial intelligence implementation in organizational learning systems across global supply chains, addressing trust deficits and transparency challenges that impede sustainable workforce development aligned with decent work principles.
Design/methodology/approach: Through interpretive analysis of contemporary AI learning implementations and organizational trust dynamics, this viewpoint synthesizes emerging practice patterns with established trust theories to propose an ethical framework for sustainable AI-driven learning ecosystems across international supply chain networks.
Findings: Organizations implementing AI learning systems without established trust frameworks experience significant employee resistance and suboptimal learning outcomes. The absence of transparent AI decision-making processes creates ethical blind spots that disproportionately impact workers in developing economies, undermining SDG 8 objectives for decent work and sustainable economic growth.
Originality: This viewpoint uniquely positions organizational trust as the foundational prerequisite for ethical AI learning integration within sustainable supply chain contexts, bridging technological advancement with human-centered development principles.
Research limitations/implications: This interpretive analysis relies on emerging practice observations rather than empirical validation, necessitating future quantitative research to test proposed frameworks across diverse organizational and cultural contexts.
Practical implications: Leaders can utilize this framework to develop trust-first AI strategies, establish transparent learning systems, and create ethical standards that promote decent work while achieving competitive advantages.
Social implications: The proposed approach directly contributes to SDG 8 by ensuring AI-driven learning systems promote inclusive economic growth, productive employment, and decent work conditions across international supply networks.},
        keywords = {Ethical AI, Supply Chain Learning, Organizational Trust, Decent Work, Algorithmic Transparency, SDG 8},
        month = {December},
        }