Ethical AI and Data Protection in the Era of Industry 5.0

Year : 2026 | Volume : 04 | 02 | Page :
    By

    Saurabh Chandravanshi,

  • Mohammed Bakhtawar Ahmed,

  1. Student, Department of Computer Science and Engineering, KK Modi University, Durg, Chhattisgarh, India
  2. Head of Department, Department of Computer Science and Engineering, KK Modi University, Durg, Chhattisgarh, India

Abstract

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: Responsible Artificial Intelligence (RAI), Industry 5.0, Data Privacy, Privacy-Enhancing Technologies (PETs), Trust-by-Design

How to cite this article:
Saurabh Chandravanshi, Mohammed Bakhtawar Ahmed. Ethical AI and Data Protection in the Era of Industry 5.0. International Journal of Information Security Engineering. 2026; 04(02):-.
How to cite this URL:
Saurabh Chandravanshi, Mohammed Bakhtawar Ahmed. Ethical AI and Data Protection in the Era of Industry 5.0. International Journal of Information Security Engineering. 2026; 04(02):-. Available from: https://journals.stmjournals.com/ijise/article=2026/view=246448


References

  1. Biju, P. R., Gayathri, O. (2024). The Indian Approach to Artificial Intelligence: An Analysis of Policy Discussions, Constitutional Values, and Regulation. AI & SOCIETY, 39(5), 2321–35.
  2. Barredo Arrieta, A., et al. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.
  3. Dimitrakopoulos, G., et al. (2024). Industry 5.0: Research Areas and Challenges With Artificial Intelligence and Human Acceptance. IEEE Industrial Electronics Magazine, 18, 43–54.
  4. European Commission. (2021). Industry 5.0: Towards a sustainable, human-centric and resilient European industry. Publications Office of the European Union.
  5. European Union. (2024). Artificial Intelligence Act (EU AI Act). Official Journal of the European Union.
  6. Greyhound Research. (2024). Trust by Design: Dissecting IBM’s Enterprise AI Governance Stack. Greyhound Research.
  7. Infosys. (2025). Market Scan Report – By Infosys Responsible AI Office: June 2025. Infosys Limited.
  8. Krause, F., &Paulheim, H. (2024). Managing human-AI collaborations within Industry 5.0 scenarios via knowledge graphs: key challenges and lessons learned. Data and Web Science Group (DWS), University of Mannheim.
  9. Laddha, A., &Agrawal, V. (2024). Industry 5.0: A transformative stage in industrial evolution. Taylor & Francis.
  10. Merchán-Cruz, E. A., et al. (2025). Trust by Design: An Ethical Framework for Collaborative Intelligence Systems in Industry 5.0. Electronics, 14(10), 1952.
  11. National Institute of Standards and Technology (NIST). (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST.
  12. OECD. (2019, 2024). Recommendation of the Council on Artificial Intelligence (OECD AI Principles). Organisation for Economic Co-operation and Development.
  13. Roy, S. (2024). Systematic Analysis of Risks in Industry 5.0 Architecture. ResearchGate.
  14. Sharma, G. (2024). Navigating Data Privacy in Industry 5.0: Advanced Strategies for Sustainability. ResearchGate.
  15. Siemens. (2024). Sustainability Report 2024. Siemens AG.
  16. TALON Project. (2024). Sharing the TALON experience: The top five integration challenges of human-centric AI systems.
  17. TCS. (2024). Building an AI-driven digital fabric for sustainable growth in industrial manufacturing. Tata Consultancy Services.
  18. UK Government. (2022). Emerging technologies and their effect on cyber security.
  19. U.S. White House Office of Science and Technology Policy. (2022). Blueprint for an AI Bill of Rights.
  20. Wang, J., et al. (2025). Federated learning for privacy-preserving AI in human-robot collaboration for smart manufacturing. Journal of Intelligent Manufacturing.
  21. Yamin, M. (2025). A Blockchain-Enhanced Zero-Trust Framework for Privacy in Industrial IoT Systems. ResearchGate.
Ahead of Print Subscription Review Article
Volume 04
02
Received 17/02/2026
Accepted 28/02/2026
Published 14/05/2026
Publication Time 86 Days
38

Login


My IP

PlumX Metrics