  Recent works in artificial intelligence fairness attempt to mitigate
discrimination by proposing constrained optimization programs that achieve
parity for some fairness statistic. Most assume availability of the class
label, which is impractical in many real-world applications such as precision
medicine, actuarial analysis and recidivism prediction. Here we consider
fairness in longitudinal right-censored environments, where the time to event
might be unknown, resulting in censorship of the class label and
inapplicability of existing fairness studies. We devise applicable fairness
measures, propose a debiasing algorithm, and provide necessary theoretical
constructs to bridge fairness with and without censorship for these important
and socially-sensitive tasks. Our experiments on four censored datasets confirm
the utility of our approach.
