A Practical Multilevel Governance Framework for Autonomous and Intelligent Systems
Autonomous and intelligent systems (AIS) facilitate a wide range of beneficial applications across a variety of different domains. However, technical characteristics such as unpredictability and lack of transparency, as well as potential unintended consequences, pose considerable challenges to the current governance infrastructure. Furthermore, the speed of development and deployment of applications outpaces the ability of existing governance institutions to put in place effective ethical-legal oversight. New approaches for agile, distributed and multilevel governance are needed. This work presents a practical framework for multilevel governance of AIS. The framework enables mapping actors onto six levels of decision-making including the international, national and organizational levels. Furthermore, it offers the ability to identify and evolve existing tools or create new tools for guiding the behavior of actors within the levels. Governance mechanisms enable actors to shape and enforce regulations and other tools, which when complemented with good practices contribute to effective and comprehensive governance.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A multilevel framework for AI governance
To realize the potential benefits and mitigate potential risks of AI, it is necessary to develop a framework of governance that conforms to ethics and fundamental human values. Although several organizations have issued …
EthicsA Theory of Multilevel Interactive Equilibrium in NeuroAI
We propose a game-theoretic framework for adaptive multi-agent intelligent systems. Unlike classical game theory, which often treats strategies as primitive objects chosen by perfectly rational agents, the proposed frame…
S5-SHB Agent: Society 5.0 enabled Multi-model Agentic Blockchain Framework for Smart Home
The smart home is a key application domain within the Society 5.0 vision for a human-centered society. As smart home ecosystems expand with heterogeneous IoT protocols, diverse devices, and evolving threats, autonomous s…
Governance Controls for AI-Generated Test Artifacts in Autonomous Software Testing
Artificial Intelligence (AI) and Large Language Models (LLMs) are increasingly used in autonomous software testing; however, AI-generated test artifacts often suffer from hallucinations, compliance violations, security r…
Data-driven and distributed governance of building facilities management using decentralized autonomous organization, digital twin, and large language models
While traditional AI and data-driven facilities management approaches have improved building operational efficiency, they remain constrained by centralized organizational structures that are vulnerable to cyber attacks, …