Abstract: Artificial intelligence (AI) is increasingly deployed in high-stakes contexts, yet their trustworthiness remains uncertain when ethical principles, technical mechanisms, and governance structures operate in isolation. This paper reviews the current landscape of trustworthy AI, including work on ensuring fairness, eliminating bias, explainability, privacy protections, trust in models, governance, and legal accountability. It further describes persistent challenges that limit reliability under real-world conditions. It identifies an operational integration gap and proposes to address this gap by the Unified Accountability Framework (UAF), a holistic, 5-tier approach for operationalizing trust in the era of foundation models. The five tiers of the UAF include Foundational Principles, Governance Structures, Lifecycle Integration, Technical Assurance Tools, and External Accountability. Trustworthy AI should be understood as an evolving sociotechnical phenomenon in which normative commitments, technical verification, and institutional oversight evolve to sustain legitimacy and adoption. In doing so, the framework provides a pathway for translating ethical intent into verifiable and governable trust across the AI lifecycle.
Keywords: Ethical AI; Trustworthy AI; Foundation Models; AI Governance; Socio-technical Trust, Accountability Frameworks
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