Trustworthy AI: A Framework for Safe and Explainable Generative AI Systems

Generative Artificial Intelligence has significant potential to transform education, healthcare, business, software engineering, cybersecurity, and many other domains. However, its benefits cannot be fully realized unless the associated risks are systematically managed. This paper proposed the Trustworthy Generative AI Lifecycle Framework (TGAI-LF) for designing and evaluating safe and explainable generative AI systems. The framework integrates seven major dimensions: 1.                Governance and Context 2.                Data and Model Assurance 3.                Safety and Robustness 4.                Explainability and Evidence 5.                Privacy and Security 6.                Human Oversight and Accountability 7.                Continuous Monitoring and Improvement The central contribution of the framework is its lifecycle-oriented approach. Trustworthiness is not treated as a single property of a model or as an explanation feature added after development. Instead, it is considered a property of the complete socio-technical AI system. The framework also provides an evaluation matrix through which organizations can collect evidence about safety, reliability, explainability, privacy, security, fairness, accountability, human oversight, and monitoring. Because this study is conceptual, future empirical research is necessary to validate the framework through case studies, user studies, security testing, and quantitative evaluation. Ultimately, trustworthy GenAI should not merely be capable of generating useful content. It should also be safe, explainable, secure, privacy-aware, accountable, robust, and subject to meaningful human oversight

[1] E. Tabassi, “Artificial Intelligence Risk Management Framework (AI RMF 1.0),” NIST AI 100-1, National Institute of Standards and Technology, Gaithersburg, MD, USA, Jan. 2023. doi: 10.6028/NIST.AI.100-1. [2] C. Autio, R. Schwartz, J. Dunietz, S. Jain, M. Stanley, E. Tabassi, P. Hall, and K. Roberts, “Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile,” NIST AI 600-1, National Institute of Standards and Technology, Gaithersburg, MD, USA, Jul. 2024. doi: 10.6028/NIST.AI.600-1. [3] OECD, “OECD AI Principles,” OECD.AI, updated 2024. [4] OECD, Advancing Accountability in AI: Governing and Managing Risks Throughout the Lifecycle for Trustworthy AI, OECD Publishing, 2023. doi: 10.1787/2448f04b-en. [5] Google DeepMind, “Holistic Safety and Responsibility Evaluation of Generative AI Systems,” arXiv:2404.14068, 2024. [6] NIST, “AI Risk Management Framework Resources,” National Institute of Standards and Technology. [7] OECD, Governing with Artificial Intelligence, OECD Publishing, 2024.