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The rule extraction literature contains the notion of a fidelity-accuracydilemma: when building an interpretable model of a black box function,optimising for fidelity is likely to reduce performance on the underlying task,and vice versa. I reassert the relevance of this dilemma for the modern fieldof explainable artificial intelligence, and highlight how it is compounded whenthe black box is an agent interacting with a dynamic environment. I thendiscuss two independent research directions - building white box agents andinterpreting black box agents - which are both coherent and worthy ofattention, but must not be conflated by researchers embarking on projects inthe domain of agent interpretability.

Am I Building a White Box Agent or Interpreting a Black Box Agent?