The focus of this paper is on policy guidance around explainable artificial intelligence (AI) or the ability to understand how AI models arrive at their outcomes. Explainability matters in human terms because it facilitates including an individual’s “right to explanation” and it also plays a role in enabling technical evaluation of AI systems. The paper begins with an examination of the meaning of explainability, concluding that the constellation of related terms serves to frustrate and confuse policy initiatives. Following a brief review of contemporary policy guidance, it argues that there is a need for greater clarity and context-specific guidance, highlighting the need to distinguish between ante hoc and post hoc explainability, especially in high-risk, high-impact contexts. The question of whether post hoc or ante hoc methods have been employed is a fundamental and often-overlooked question in policy. The paper argues that the question of which method should be employed in a given context, along with the requirement for human-level understanding, is a key challenge that policy makers need to address. A taxonomy for how explainability can be operationalized in AI policy is proposed and a series of recommendations is set forth.
