Ethical AI and Data Protection in the Era of Industry 5.0 - Saurabh Chandravanshi; Mohammed Bakhtawar Ahmed (International Journal of Information Security Engineering, 2026)
This paper provides a comprehensive analysis of the critical intersection between Responsible AI (RAI), data privacy, and the Industry 5.0 paradigm. Industry 5.0, defined by its human-centric, sustainable, and resilient pillars, introduces a fundamental paradox: its core requirement for human-AI collaboration necessitates the collection and processing of granular human data, creating direct conflicts with emerging global data privacy and AI regulations. This research utilizes a systematic integrative review methodology, analyzing peer-reviewed literature from Scopus, IEEE, Springer, and Elsevier, alongside key policy documents (e.g., EU AI Act 2024, NIST AI RMF, India's DPDP Act 2023) and industry case studies. We argue that addressing this conflict requires a novel, integrated "Trust-by-Design Stack." This theoretical framework combines (1) organizational governance (Privacy-by- Design), (2) Privacy-Enhancing Technologies (PETs) like Federated Learning and Differential Privacy, (3) interpretability frameworks (Explainable AI), and (4) secure network architectures (Zero-Trust). The analysis demonstrates a global schism in governance philosophies—from the EU's rights-based model to the US's innovation-centric approach and Asia's state-led strategic models. Case studies of IBM, Siemens, Microsoft, Infosys, and Tesla reveal archetypal corporate strategies for navigating this fragmented landscape. The paper concludes that RAI is not merely a compliance burden but the core enabling infrastructure for Industry 5.0, with its successful implementation dependent on a symbiotic fusion of policy, technology, and organizational governance.
Keywords: Industry 5.0; Privacy-Enhancing Technologies (PETs); Responsible Artificial Intelligence (RAI); Trust-by-Design; data privacy