  When used in complex engineered systems, such as communication networks,
artificial intelligence (AI) models should be not only as accurate as possible,
but also well calibrated. A well-calibrated AI model is one that can reliably
quantify the uncertainty of its decisions, assigning high confidence levels to
decisions that are likely to be correct and low confidence levels to decisions
that are likely to be erroneous. This paper investigates the application of
conformal prediction as a general framework to obtain AI models that produce
decisions with formal calibration guarantees. Conformal prediction transforms
probabilistic predictors into set predictors that are guaranteed to contain the
correct answer with a probability chosen by the designer. Such formal
calibration guarantees hold irrespective of the true, unknown, distribution
underlying the generation of the variables of interest, and can be defined in
terms of ensemble or time-averaged probabilities. In this paper, conformal
prediction is applied for the first time to the design of AI for communication
systems in conjunction to both frequentist and Bayesian learning, focusing on
demodulation, modulation classification, and channel prediction.
