  Binary decision aids, such as alerts, are a simple and widely used form of
automation. The formal analysis of a user's task performance with an aid sees
the process as the combination of information from two detectors who both
receive input about an event and evaluate it. The user's decisions are based on
the output of the aid and on the information, the user obtains independently.
We present a simple method for computing the maximal benefits a user can derive
from a binary aid as a function of the user's and the aid's sensitivities.
Combining the user and the aid often adds little to the performance the better
detector could achieve alone. Also, if users assign non-optimal weights to the
aid, performance may drop dramatically. Thus, the introduction of a valid aid
can actually lower detection performance, compared to a more sensitive user
working alone. Similarly, adding a user to a system with high sensitivity may
lower its performance. System designers need to consider the potential adverse
effects of introducing users or aids into systems.
