  Technology-assisted review (TAR) workflows based on iterative active learning
are widely used in document review applications. Most stopping rules for
one-phase TAR workflows lack valid statistical guarantees, which has
discouraged their use in some legal contexts. Drawing on the theory of quantile
estimation, we provide the first broadly applicable and statistically valid
sample-based stopping rules for one-phase TAR. We further show theoretically
and empirically that overshooting a recall target, which has been treated as
innocuous or desirable in past evaluations of stopping rules, is a major source
of excess cost in one-phase TAR workflows. Counterintuitively, incurring a
larger sampling cost to reduce excess recall leads to lower total cost in
almost all scenarios.
