A Cross-Sector Review of Ethical Issues in Technology Adoption

  • Authors

    • A. Isabella Research Scholar, Department of Computer Science, Hindustan Institute of Technology and Science, Chennai, Tanil Nadu, India. Author
    • R. Nandhakumar Research Scholar, Department of Computer Science, Hindustan Institute of Technology and Science, Chennai, Tanil Nadu, India. Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-2021PI7B4M

    Published 01-04-2021

  • Ethical Technology Adoption, Artificial Intelligence Ethics, Digital Governance, Privacy, Algorithmic Bias, Cross-Sector Analysis, Responsible Innovation, Technology Policy

    Issue

    Vol. 4 No. 1 (2021)

    Section

    Articles

    How to Cite

    [1]
    I. A and N. R, “A Cross-Sector Review of Ethical Issues in Technology Adoption”, IJETMR, vol. 4, no. 1, pp. 01–16, Jan. 2021, doi: 10.67228/30715636/IJETMR-2021PI7B4M.

  • Abstract

    Rapid adoption of technologies like Artificial Intelligence, big data, IoT, blockchain, automation, and cloud computing has transformed industries by improving efficiency, innovation, and scalability. However, this growth has also introduced significant ethical challenges, including data privacy breaches, algorithmic bias, surveillance, workforce displacement, lack of transparency, and digital inequality.These issues affect multiple sectors such as healthcare, finance, education, manufacturing, and governance, though their impact varies—for example, patient data security in healthcare, fraud prevention in finance, and job displacement in manufacturing. The study highlights that ethical concerns are interconnected, with problems like privacy linked to cybersecurity and bias tied to transparency.To address these challenges, the research proposes an ethical adoption model based on fairness, accountability, transparency, privacy, inclusivity, and sustainability. It emphasizes the importance of organizational ethics, stakeholder involvement, and regulatory alignment. Overall, the study argues that ethical considerations are essential for sustainable technological progress and should be integrated into all stages of technology development and implementation.

  • References

    [1] Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707.

    [2] Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1–21.

    [3] Bostrom, N., & Yudkowsky, E. (2014). The ethics of artificial intelligence. In The Cambridge Handbook of Artificial Intelligence. Cambridge University Press.

    [4] European Parliament and Council of the European Union. (2016). General Data Protection Regulation (GDPR). Official Journal of the European Union.

    [5] Tavani, H. T. (2016). Ethics and Technology: Controversies, Questions, and Strategies for Ethical Computing (5th ed.). Wiley.

    [6] Zuboff, S. (2019). The Age of Surveillance Capitalism. PublicAffairs.

    [7] Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732.

    [8] O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishing.

    [9] Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems, 14(3), 330–347.

    [10] Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W.W. Norton & Company.

    [11] Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280.

    [12] Pasquale, F. (2015). The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press.

    [13] Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.

    [14] Dignum, V. (2019). Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. Springer.

    [15] United Nations Educational, Scientific and Cultural Organization (UNESCO). (2021). Recommendation on the Ethics of Artificial Intelligence. UNESCO Publishing.

  • Downloads