Public attention towards explainability of artificial intelligence (AI) systems has been rising in recent years to offer methodologies for human oversight. This has translated into the proliferation of research outputs, such as from Explainable AI, to enhance transparency and control for system debugging and monitoring, and intelligibility of system process and output for user services. Yet, such outputs are difficult to adopt on a practical level due to a lack of a common regulatory baseline, and the contextual nature of explanations. Governmental policies are now attempting to tackle such exigence, however it remains unclear to what extent published communications, regulations, and standards adopt an informed perspective to support research, industry, and civil interests. In this study, we perform the first thematic and gap analysis of this plethora of policies and standards on explainability in the EU, US, and UK. Through a rigorous survey of policy documents, we first contribute an overview of governmental regulatory trajectories within AI explainability and its sociotechnical impacts. We find that policies are often informed by coarse notions and requirements for explanations. This might be due to the willingness to conciliate explanations foremost as a risk management tool for AI oversight, but also due to the lack of a consensus on what constitutes a valid algorithmic explanation, and how feasible the implementation and deployment of such explanations are across stakeholders of an organization. Informed by AI explainability research, we conduct a gap analysis of existing policies, leading us to formulate a set of recommendations on how to address explainability in regulations for AI systems, especially discussing the definition, feasibility, and usability of explanations, as well as allocating accountability to explanation providers.

翻译：近年来，公众对人工智能系统可解释性的关注度持续上升，旨在为人类监督提供方法论支撑。这种关注已转化为大量研究成果的涌现——例如可解释人工智能领域的研究，通过增强系统调试与监控的透明度与可控性，提升系统运行过程与输出结果对用户服务的可理解性。然而，由于缺乏统一的监管基线及解释本身的语境依赖性，这些成果在实践层面难以落地应用。各国政府政策正致力于应对这一现实需求，但尚不明确已发布的通讯、法规与标准在多大程度上采纳了能支撑研究、产业与公众利益的知情视角。本研究首次对欧盟、美国及英国在可解释性领域的大量政策与标准进行主题分析与差距分析。通过系统梳理政策文件，我们首先描绘了政府监管在AI可解释性及其社会技术影响方面的发展轨迹。研究发现，政策制定常基于对解释的粗放概念化认知与要求，这既源于政策制定者优先将解释作为AI风险管控工具的倾向，也归因于学界对有效算法解释的构成要件、以及跨组织利益相关方实施部署可行性的共识缺失。基于AI可解释性研究成果，我们对现有政策开展差距分析，继而提出系列建议：涵盖AI系统监管中如何界定可解释性、提升解释的可行性与实用性，以及明确解释提供方的问责机制。