AI governance efforts increasingly rely on audit standards: agreed-upon practices for conducting audits. However, poorly designed standards can hide and lend credibility to inadequate systems. We explore how an audit standard’s design influences its effectiveness through a case study of ASB 018, a standard for auditing probabilistic genotyping software---software that the U.S. criminal legal system increasingly uses to analyze DNA samples. Through qualitative analysis of ASB 018 and five audit reports, we identify numerous gaps between the standard's desired outcomes and the auditing practices it enables. For instance, ASB 018 envisions that compliant audits establish restrictions on software use based on observed failures. However, audits can comply without establishing such boundaries. We connect these gaps to the design of the standard’s requirements such as vague language and undefined terms. We conclude with recommendations for designing audit standards and evaluating their effectiveness.
ACM CHI Conference on Human Factors in Computing Systems