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Public attention towards explainability of artificial intelligence (AI)systems has been rising in recent years to offer methodologies for humanoversight. This has translated into the proliferation of research outputs, suchas from Explainable AI, to enhance transparency and control for systemdebugging and monitoring, and intelligibility of system process and output foruser services. Yet, such outputs are difficult to adopt on a practical leveldue to a lack of a common regulatory baseline, and the contextual nature ofexplanations. 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 andgap analysis of this plethora of policies and standards on explainability inthe EU, US, and UK. Through a rigorous survey of policy documents, we firstcontribute an overview of governmental regulatory trajectories within AIexplainability and its sociotechnical impacts. We find that policies are ofteninformed by coarse notions and requirements for explanations. This might be dueto the willingness to conciliate explanations foremost as a risk managementtool for AI oversight, but also due to the lack of a consensus on whatconstitutes a valid algorithmic explanation, and how feasible theimplementation and deployment of such explanations are across stakeholders ofan organization. Informed by AI explainability research, we conduct a gapanalysis of existing policies, leading us to formulate a set of recommendationson how to address explainability in regulations for AI systems, especiallydiscussing the definition, feasibility, and usability of explanations, as wellas allocating accountability to explanation providers.

Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UK