  We initiate the study of fairness for ordinal regression. We adapt two
fairness notions previously considered in fair ranking and propose a strategy
for training a predictor that is approximately fair according to either notion.
Our predictor has the form of a threshold model, composed of a scoring function
and a set of thresholds, and our strategy is based on a reduction to fair
binary classification for learning the scoring function and local search for
choosing the thresholds. We provide generalization guarantees on the error and
fairness violation of our predictor, and we illustrate the effectiveness of our
approach in extensive experiments.
