Increased delegation of commercial, scientific, governmental, and personal activities to AI agents -- systems capable of pursuing complex goals with limited supervision -- may exacerbate existing societal risks and introduce new risks. Understanding and mitigating these risks involves critically evaluating existing governance structures, revising and adapting these structures where needed, and ensuring accountability of key stakeholders. Information about where, why, how, and by whom certain AI agents are used, which we refer to as visibility, is critical to these objectives. In this paper, we assess three categories of measures to increase visibility into AI agents: agent identifiers, real-time monitoring, and activity logging. For each, we outline potential implementations that vary in intrusiveness and informativeness. We analyze how the measures apply across a spectrum of centralized through decentralized deployment contexts, accounting for various actors in the supply chain including hardware and software service providers. Finally, we discuss the implications of our measures for privacy and concentration of power. Further work into understanding the measures and mitigating their negative impacts can help to build a foundation for the governance of AI agents.

翻译：商业、科学、政府及个人活动日益委托给人工智能代理——这些系统能够在有限监督下追求复杂目标——可能加剧现有社会风险并引入新风险。理解并缓解这些风险需要批判性评估现有治理结构，在必要时修订和调整这些结构，并确保关键利益相关方的问责制。关于哪些人工智能代理在何处、为何、如何以及由谁使用的信息，我们称之为可见性，对于实现这些目标至关重要。本文评估了三类提升人工智能代理可见性的措施：代理标识符、实时监控和活动日志记录。针对每类措施，我们概述了在侵入性和信息丰富性方面各异的潜在实施方案。我们分析了这些措施如何适用于从集中化到去中心化的一系列部署环境，并考虑了供应链中各参与者（包括硬件和软件服务提供商）的作用。最后，我们讨论了这些措施对隐私和权力集中的影响。进一步研究这些措施并减轻其负面影响，有助于为人工智能代理的治理奠定基础。