Scholarly research papers from leading academic journals, conferences, and research institutions contributing to AI governance knowledge.
Sorted by year and impact
Highlights importance of systematically identifying bias and engaging mitigation activities throughout AI model lifecycle with practical strategies.
Defines accountability in AI context and analyses its architecture through compliance, reporting, oversight and liability goals with practical frameworks.
Identifies three dimensions of AI implementation in public sector: technology-deterministic, data-induced, and organizational transformation approaches.
Proposes AI risk management maturity model building on NIST AI RMF, enabling organisations to assess and advance their AI governance capabilities.
Proposes comprehensive framework for AI safety assurance combining formal verification, runtime monitoring, and safety-critical engineering practices.
Comparative analysis of AI regulatory approaches in healthcare across major jurisdictions, examining standards, approval processes, and oversight mechanisms.
Reviews AI applications in safety and reliability engineering, examining both opportunities and challenges in using AI for safety-critical systems.
Reviews privacy-preserving techniques for AI in healthcare including federated learning, differential privacy, and secure multi-party computation.
Provides insights into what organisations consider important in transparency and explainability of AI systems, bridging ethics and engineering.
Comprehensive study on trustworthy AI elements and integration of explainable AI methodologies across diverse applications and domains.
Proposes comprehensive approaches for understanding and mitigating AI bias in healthcare to advance health equity and reduce disparities.
Examines unique opportunities and challenges SMEs face in AI adoption, providing strategic guidance for successful integration.
Comprehensive systematic review synthesizing research on algorithmic bias with integrated framework and agenda for future research directions.
Develops actionable properties for designing AI systems under meaningful human control with practical guidance for developers and organisations.
Develops comprehensive conceptual framework for regulating AI based on systematic review of literature on AI governance and regulatory theory.
Critical evaluation of over 80 AI ethics guidelines from around the world, examining their effectiveness, convergence, and implementation challenges.
Examines relationship between GDPR and AI, analysing how data protection regulation impacts AI development, deployment, and governance in the EU.
Reviews challenges and approaches to data governance for AI systems, proposing comprehensive framework for trustworthy data management.
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