Bridging Governance and Technology: Data, Models, and Responsibility in Regulation

Shaina Raza, Mahveen Raza

Keywords: ai goverance, responsible ai

Abstract: Generative AI systems have seen unprecedented adoption, raising urgent questions about their safety and accountability. This paper emphasizes that Responsible Generative AI cannot be achieved through isolated fixes, but requires a multi-layer synthesis of technical, regulatory, and design approaches. We survey four pillars of this roadmap: (1) workflow-level defenses, such as sandboxing and provenance tracking, that confine models within safe operational boundaries; (2) evaluation protocols and compliance criteria inspired by emerging regulations, including risk assessments, logging, and third-party audits; (3) liability frameworks and international coordination mechanisms that clarify responsibility when AI systems cause harm; and (4) the ``AI Scientist" paradigm, which reimagines AI as non-agentic and uncertainty-aware, enforcing safe operating envelopes through design patterns like planner–executor separation and human-in-the-loop oversight. Taken together, these perspectives highlight how technical safeguards, governance evidence, and safe-by-design paradigms can converge into a coherent strategy for the sustainable and trustworthy deployment of generative AI. Through this review article, we synthesize multidisciplinary insights to guide the development of safer GenAI systems.

Track: Track 2: ML by Muslim Authors

Submission Number: 21