  In response to everyday queries, humans explicitly signal uncertainty and
offer alternative answers when they are unsure. Machine learning models that
output calibrated prediction sets through conformal prediction mimic this human
behaviour; larger sets signal greater uncertainty while providing alternatives.
In this work, we study the usefulness of conformal prediction sets as an aid
for human decision making by conducting a pre-registered randomized controlled
trial with conformal prediction sets provided to human subjects. With
statistical significance, we find that when humans are given conformal
prediction sets their accuracy on tasks improves compared to fixed-size
prediction sets with the same coverage guarantee. The results show that
quantifying model uncertainty with conformal prediction is helpful for
human-in-the-loop decision making and human-AI teams.
