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Guardians of green: Legal implications and best practices in AI-enabled CSR for environmental sustainability in India

Zubair Ahmed, Naushad Anwar, Amlan De, Rahul Shaw

Abstract


This research critically examines the intricate intersection of Artificial Intelligence (AI) and Corporate Social Responsibility (CSR), specifically focusing on environmental sustainability. It elucidates the legal implications and optimal practices inherent in their amalgamation. The study underscores the transformative potential for corporations to adopt the “Guardians of Green” role by leveraging AI technologies, such as data analytics, real-time monitoring, and predictive modelling, to enhance the efficacy of environmental stewardship initiatives. A meticulous analysis of the prevailing legal frameworks governing the integration of AI within CSR is conducted, emphasizing compliance paradigms and contemplating potential regulatory advancements that may shape corporate conduct. The paper also delineates best practices for corporations seeking to maximize the environmental impact of their CSR initiatives through the strategic integration of AI, examining its role in optimizing supply chains, improving energy efficiency, and revolutionizing waste management processes. Ethical considerations intrinsic to AI-driven sustainability practices are scrutinized, highlighting the importance of transparent and ethically responsible applications. By amalgamating legal analysis, empirical case studies, and practical insights, this research seeks to provide comprehensive guidance for corporations, policymakers, and legal professionals navigating the evolving landscape of AI-infused CSR for environmental sustainability in India. It establishes a scholarly foundation for future inquiries and discussions on the legal and ethical dimensions underpinning corporate environmental responsibility in the contemporary era of autonomous intelligence.


Keywords


sustainable development; corporate social responsibility; artificial intelligence; legal regulation; best practices

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References


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DOI: https://doi.org/10.32629/jai.v7i5.1572

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