  Principled accountability in the aftermath of harms is essential to the
trustworthy design and governance of algorithmic decision making. Legal theory
offers a paramount method for assessing culpability: putting the agent 'on the
stand' to subject their actions and intentions to cross-examination. We show
that under minimal assumptions automated reasoning can rigorously interrogate
algorithmic behaviors as in the adversarial process of legal fact finding. We
model accountability processes, such as trials or review boards, as
Counterfactual-Guided Logic Exploration and Abstraction Refinement (CLEAR)
loops. We use the formal methods of symbolic execution and satisfiability
modulo theories (SMT) solving to discharge queries about agent behavior in
factual and counterfactual scenarios, as adaptively formulated by a human
investigator. In order to do so, for a decision algorithm $\mathcal{A}$ we use
symbolic execution to represent its logic as a statement $\Pi$ in the decidable
theory $\texttt{QF_FPBV}$. We implement our framework and demonstrate its
utility on an illustrative car crash scenario.
