Bayesian Selection Policies for Human-in-the-Loop Anomaly Detectors with Applications in Test Security.

This article investigates the problem of automatically flagging test takers who exhibit atypical responses or behaviors for further review by human experts. The objective is to develop a selection policy that maximizes the expected number of test takers correctly identified as warranting additional scrutiny while maintaining a manageable volume of reviews per test administration. The selection procedure should learn from the outcomes of the expert reviews. Since typically only a fraction of test takers are reviewed, this leads to a semi-supervised learning problem. The latter is formalized in a Bayesian setting, and the corresponding optimal selection policy is derived. Since calculating the policy and the underlying posterior distributions is computationally infeasible, a variational approximation and three heuristic selection policies are proposed. These policies are informed by properties of the optimal policy and correspond to different exploration/exploitation trade-offs. The performance of the approximate policies is assessed via numerical experiments using both synthetic and real-world data and is compared with procedures based on off-the-shelf algorithms as well as theoretical performance bounds.