  The U.S. criminal legal system increasingly relies on software output to
convict and incarcerate people. In a large number of cases each year, the
government makes these consequential decisions based on evidence from
statistical software -- such as probabilistic genotyping, environmental audio
detection, and toolmark analysis tools -- that defense counsel cannot fully
cross-examine or scrutinize. This undermines the commitments of the adversarial
criminal legal system, which relies on the defense's ability to probe and test
the prosecution's case to safeguard individual rights.
  Responding to this need to adversarially scrutinize output from such
software, we propose robust adversarial testing as an audit framework to
examine the validity of evidentiary statistical software. We define and
operationalize this notion of robust adversarial testing for defense use by
drawing on a large body of recent work in robust machine learning and
algorithmic fairness. We demonstrate how this framework both standardizes the
process for scrutinizing such tools and empowers defense lawyers to examine
their validity for instances most relevant to the case at hand. We further
discuss existing structural and institutional challenges within the U.S.
criminal legal system that may create barriers for implementing this and other
such audit frameworks and close with a discussion on policy changes that could
help address these concerns.
