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Oversight agencies, supervisory organizations, and regulatory authorities for AI governance.
Also in Legal & Regulatory
This process is essential for optimizing the regulatory mechanism, emphasizing the iterative evaluation and dynamic adjustment of ethical regulatory processes. This approach ensures that review procedures are continually refined to address emerging ethical issues and operational challenges.
This process may requires the establishment of new institutions. For example, the improvement unit, acting as a sensor-like role, identifies and compiles information on potential enhancements within the regulatory process (Path 12) and communicates it (Path 13) to the administrative unit. Administrative units are departments within ethical regulatory agencies responsible for daily management, serving as controllers in the design and optimization of the regulatory mechanism. They analyze and refine the mechanism, while all entities involved in processes (1) and (2) assume actuatorlike roles to implement the new mechanisms (Path 14). In further practical implementation, iterative evaluation begins by defining the evaluation criteria based on current ethical guidelines, regulatory standards, and performance metrics. Each iteration tests the effectiveness of these procedures and collects feedback from stakeholders. Feedback is analyzed to identify areas for improvement, allowing for necessary process optimization. Dynamic adjustment ensures that these changes are implemented to keep pace with evolving conditions. This process is repeated to progressively enhance the responsiveness, effectiveness, and adaptability of regulatory mechanism. Just as control algorithms underpin the operation of process control systems, the effective implementation of the three feedback processes relies on feasible rules and steps. This represents one of the most critical tasks for government regulatory agencies in translating principles into practice.
Ethical review process
This is the primary procedure in ethical regulation, where AI systems serve as inputs to the process (as indicated by ‘‘AI in’’ and Path 1 in Fig. 2). Because AI systems are rapidly evolving [24], they require real-time monitoring and feedback throughout their entire lifecycle to facilitate updates and iterations (represented by ‘‘AI 1’’, ‘‘AI 2’’, ... , ‘‘AI n’’ in Fig. 2). This process ensures that the final application state of AI system adheres to ethical requirements in real-world (as shown by AI out and Path 2 in Fig. 2)
3.1.2 Regulatory BodiesIncentive and punishment process
This process draws on behavioral psychology’s reinforcement concept, using rewards and penalties as reinforcement methods to ensure the effective transformation of the regulatory mechanism in practice.
3.1.5 Enforcement MechanismsDeveloping an Ethical Regulatory Framework for Artificial Intelligence: Integrating Systematic Review, Thematic Analysis, and Multidisciplinary Theories
Wang, Jian; Huo, Yujia; Mahe, Jinli; Ge, Zongyuan; Liu, Zhangdaihong; Wang, Wenxin; Zhang, Lin (2024)
Artificial intelligence (AI) ethics has emerged as a global discourse within both academic and policy spheres. However, translating these principles into concrete, real-world applications for AI development remains a pressing need and a significant challenge. This study aims to bridge the gap between principles and practice from a regulatory government perspective and promote best practices in AI governance. To this end, we developed the Ethical Regulatory Framework for AI (ERF-AI) to guide regulatory bodies in constructing mechanisms, including role setups, procedural configurations, and strategy design. The framework was developed through a systematic review, thematic analysis, and the integration of interdisciplinary concepts. A comprehensive search was conducted across four electronic databases (PubMed, IEEE Xplore, Web of Science, and Scopus) and four additional sources containing AI standards and guidelines from various countries and international organizations, focusing on studies published from 2014 to 2024. Thematic analysis identified and refined key themes from the included literature and integrated concepts from process control theory, computer science, organizational management, information technology, and behavioral psychology. This study adhered to the PRISMA guidelines and employed NVivo for thematic analysis. The resulting framework encompasses 23 themes, particularly emphasizing three feedback-loop processes: the ethical review process, the incentive and penalty process, and the mechanism improvement process, offering theoretical guidance for the construction of ethical regulatory mechanisms. Based on this framework, a seven-step process and case examples for mechanism design are presented, enhancing the practicality of ERF-AI in developing ethical regulatory mechanisms. Future research is expected to explore customization of the framework to remain responsive to emerging AI trends and challenges, supported by empirical studies and rigorous testing for further refinement and expansion. © 2024 IEEE.
Operate and Monitor
Running, maintaining, and monitoring the AI system post-deployment
Governance Actor
Regulator, standards body, or oversight entity shaping AI policy
Govern
Policies, processes, and accountability structures for AI risk management
Primary
6.5 Governance failure