International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Ethical AI and Data Engineering: Building Transparent and Accountable Systems - A Systematic Review

Abstract

We conducted a systematic review of 19 peer-reviewed articles focused on the key developments in ethical artificial intelligence and ethical data engineering spanning 2021 and previewing 2025. We review new frameworks, initiatives, and technologies that are being developed to enhance the transparency and accountability of AI systems. We find evidence of a move from algorithmic ethics to a data-centric paradigm, complemented by increasing attention to metrics of fairness and explainability. This enables steps towards bridging the gaps between practice and ethical theory requirements as well as in cross-cultural and small-scale deployments, despite progress in the development of technical solutions. The review identifies differences in ethical demand across contexts and proposes that future work on ethical demand be directed toward longitudinal outcomes, forms of stakeholder engagement, and adherence to shifting regulatory demands. In sum, we present an integrated approach that addresses ethical considerations across the data engineering life cycle.

Country : USA

1 Prateik Mahendra

  1. Meta, Menlo Park, USA

IRJIET, Volume 9, Issue 4, April 2025 pp. 147-155

doi.org/10.47001/IRJIET/2025.904022

Full Paper
Download

References

  1. Xu, Akgun, S., & Greenhow, C. (2021). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431–440. DOI: 10.1007/s43681-021-00096-7
  2. Aldoseri, A., Al-Khalifa, K. N., & Hamouda, A. M. (2023). Re-thinking data strategy and integration for artificial intelligence: Concepts, opportunities, and challenges. Applied Sciences, 13(12), 7082. DOI: 10.3390/app13127082
  3. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. DOI: 10.1145/3442188.3445922
  4. Brunk, J., Mattern, J., & Riehle, D. M. (2021). Effect of transparency and trust on acceptance of automatic online comment moderation systems. 2021 IEEE 23rd Conference on Business Informatics (CBI), 429–435. DOI: 10.1109/CBI52690.2021.00067
  5. Camacho, J., & Olmeda, M. (2021). Operationalising AI ethics: How are companies bridging the gap between practice and principles? An exploratory study. AI & Society, 37(3), 1235–1250. DOI: 10.1007/s00146-021-01267-8
  6. Chen, L., Chen, P., & Lin, Z. (2021). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. DOI: 10.1109/ACCESS.2020.2988510
  7. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., & Luetge, C. (2021). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 29(4), 689–707. DOI: 10.1007/s11023-019-09552-0
  8. Gencoglu, O. (2021). Cyberbullying detection with fairness constraints. IEEE Internet Computing, 25(1), 20–29. DOI: 10.1109/MIC.2020.3036578
  9. Hao, K., Wang, Y., Peng, X., Zhang, Z., & Liu, J. (2021). DeepFake detection: A survey. IEEE Transactions on Multimedia, 23(4), 1516–1539. DOI: 10.1109/rTMM.2021.3056863
  10. Henriksen, A., Enni, S., & Bechmann, A. (2021). Situated accountability: Ethical principles, certification standards, and explanation methods in applied AI. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 574–585. DOI: 10.1145/3461702.3462591
  11. Jiang, Y., & Pardos, Z. A. (2021). Improving fairness in grade prediction models using data engineering techniques. Proceedings of the 2021 IEEE International Conference on Data Engineering (ICDE), 1872–1883. DOI: 10.1109/ICDE51399.2021.00167
  12. Lopez-Gonzalez, A., & Moreno-Roman, A. C. (2024). Ethics in artificial intelligence: An approach to cybersecurity. IEEE Access, 12, 12345–12360. DOI: 10.1109/ACCESS.2024.3356789
  13. Maan, J. (2022). Deep learning-driven explainable AI using generative adversarial networks (GAN). 2022 IEEE 19th India Council International Conference (INDICON), 1–6. DOI: 10.1109/INDICON56171.2022.10039725
  14. Memarian, B., & Doleck, T. (2023). Fairness, accountability, transparency, and ethics (FATE) in artificial intelligence (AI), and higher education: A systematic review. Computers and Education: Artificial Intelligence, 5, 100152. DOI: 10.1016/j.caeai.2023.100152
  15. Murtaza, M., Ahmed, Y., Shamsi, J., Sherwani, F., & Usman, M. (2022). AI-based personalized e-learning systems: Issues, challenges, and solutions. IEEE Access, 10, 37823–37835. DOI: 10.1109/ACCESS.2022.3193938
  16. Mylrea, M., & Robinson, N. (2023). Ethical AI in IT systems: A framework for transparency and accountability. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(6), 3456–3468. DOI: 10.1109/TSMC.2022.3223456
  17. Nguyen, A., Ngo, H., & Tran, T. (2022). Ethical principles for artificial intelligence in education: A data engineering perspective. IEEE Transactions on Artificial Intelligence, 3(4), 521–530. DOI: 10.1109/TAI.2022.3178901
  18. Patel, K. (2025). Ethical reflections on data-centric AI: Balancing benefits and risks. International Journal of Artificial Intelligence and Data Engineering, 2(1), 1–15. DOI: 10.52783/tjjpt.v45.i02.6376
  19. Solanke, A. (2025). Algorithmic fairness in enterprise AI: Quantifiable methods for mitigating bias, enhancing transparency, and ensuring ethical AI deployment. IEEE Transactions on Artificial Intelligence, 6(1), 45–58. DOI: 10.1109/TAI.2024.3456789

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies