TL;DR
It is argued that design is the primary site at which ethical commitments become operational in AI systems, with regulation serving as the institutional scaffolding that sustains design discipline, in a trilateral framework that systematically integrates ethics, design, and regulation.
Abstract Artificial intelligence (AI) is increasingly integrated into health research, creating ethical questions that extend beyond privacy, consent, bias, transparency, accountability, and safety. Existing bioethical principles and AI governance frameworks provide essential foundations but complex health-data ecosyst...
The evidence shows strong convergence around fairness, transparency, privacy, accountability, human oversight, safety and inclusiveness, but weaker agreement on implementation, and an integrated framework for developing, deploying and monitoring AI systems in ways that are lawful, transparent, accountable, inclusive an...
The study demonstrates how MHC can be translated from an ethical principle into governance practice and shows that meaningful control depends not only on human involvement in decisions, but also on defined purposes and boundaries, operational procedures for review and correction, legal safeguards, and institutional cap...
The article develops a Five-Principle Workplace AI Safeguard Matrix that links each principle to design evidence, organisational evidence and worker-facing evidence, and proposes Occupational AI Safeguard Rounds as a recurring interdisciplinary review practice connecting ethics, engineering, participation and redress.
By showing that AI use intensifies rather than replaces human judgment, the study underscores the continued centrality of human responsibility in public decision-making and suggests that effective AI governance requires a shift from restrictive, compliance-focused approaches towards enabling and adaptive strategies.
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