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Penalties, sanctions, taxes, incentives, and enforcement powers backed by state authority.
Also in Legal & Regulatory
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.
In this process, reviewers and users act as sensor-like roles, providing information to the disciplinary body; reviewers convey the outcomes of ethical reviews (Path 10), while users report on ethical performance in practical applications (Path 9 upward). Based on this information, the disciplinary body assumes both actuatorlike and controller-like roles, assessing whether to impose rewards or penalties and determining their appropriate magnitude, while also incentivizing or penalizing developers (Path 11) and providing compensation if users experience ethical harm (Path 9 downward). In the event of legal disputes, the judicial branch and law enforcement agencies (represented as legal authorities in Fig. 2) assume controllerlike and actuator-like roles, respectively. Another scenario involves the illegal use of the AI system by users, which may also pose ethical risks; in this case, the users are the ones subject to penalties. In the further practical implementation, specific standards and levels for rewards, penalties, and compensation should be established, particularly a comprehensive accountability system, to ensure clear boundaries for enforcement.
Reasoning
Establishes formal governance framework defining incentive and punishment procedures for organizational accountability.
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 BodiesMechanism improvement process
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.
3.1.2 Regulatory BodiesDeveloping 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.
Other (outside lifecycle)
Outside the standard AI system lifecycle
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