From Ethical Principles to Technical Safeguards: A Unified Framework for Safe and Human-Centered Artificial Intelligence
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V4I5P103Keywords:
Safe Artificial Intelligence, AI Ethics, Human-Centered AI, AI Safety, Ethical AI Design, Technical Alignment, Governance Framework, Reward Specification, Privacy-Preserving AI, Trustworthy AI, Accountability, TransparencyAbstract
The growing use of artificial intelligence in healthcare, employment, and business has raised significant concerns regarding safety, ethics, and societal impact. While prior work offers ethical guidelines and technical solutions independently, a gap remains between ethical principles and practical system design. This study presents a unified framework that integrates ethical values, technical safeguards, and governance mechanisms to support safe and human-centered artificial intelligence. The framework maps principles such as fairness, accountability, transparency, and non-maleficence to engineering practices including safe reward design, explainable models, robustness testing, and privacy-preserving techniques. Governance and regulatory alignment are incorporated as continuous components of the AI lifecycle. Use-case analyses demonstrate how ethical objectives can be operationalized across real-world domains. The results emphasize that AI safety must be treated as a socio-technical process combining technical alignment with institutional oversight. This work contributes a practical approach for translating ethical commitments into trustworthy and resilient AI systems.
References
[1] Leike, J., Martic, M., Krakovna, V., Ortega, P. A., Everitt, T., Lefrancq, A., ... & Legg, S. (2017). AI safety gridworlds. arXiv preprint arXiv:1711.09883.
[2] Leslie, D. (2019). Understanding artificial intelligence ethics and safety. arXiv preprint arXiv:1906.05684.
[3] Shneiderman, B. (2020). Bridging the gap between ethics and practice: guidelines for reliable, safe, and trustworthy human-centered AI systems. ACM Transactions on Interactive Intelligent Systems (TiiS), 10(4), 1-31.
[4] Omohundro, S. M. (2018). The basic AI drives. In Artificial intelligence safety and security (pp. 47-55). Chapman and Hall/CRC.
[5] Bostrom, N., & Yudkowsky, E. (2018). The ethics of artificial intelligence. In Artificial intelligence safety and security (pp. 57-69). Chapman and Hall/CRC.
[6] Howard, J. (2019). Artificial intelligence: Implications for the future of work. American journal of industrial medicine, 62(11), 917-926.
[7] Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. In Artificial intelligence in healthcare (pp. 295-336). Academic Press.
[8] Veale, M., & Borgesius, F. Z. (2021). Demystifying the draft EU artificial intelligence act. arXiv preprint arXiv:2107.03721.
[9] Methuku, V., Kamatala, S., & Myakala, P. K. (2021). Bridging the Ethical Gap: Privacy-Preserving Artificial Intelligence in the Age of Pervasive Data.
[10] Myakala, P. K. (2019). How Machine Learning Simplifies Business Decision-Making. Complexity International Journal (CIJ), 23(03), 407-410.
[11] Aliman, N. M., Kester, L., Werkhoven, P., & Ziesche, S. (2019). Sustainable AI safety?. Delphi, 2, 226.
[12] Kamatala, S., & Naayini, P. (2022). Towards Resilient Intelligence: Transferable and Trustworthy AI for Real-World Systems. Available at SSRN 5329895.
[13] Nellutla, N. (2021). Scaling Telemedicine Platforms with Cloud-Native DevOps: An Architecture for Reliable Patient Services. American International Journal of Computer Science and Technology, 3(2), 30-38.
