Decision processes in AI and operations research often involve parametric optimization problems, whose unknown parameters must be inferred from data. The Predict-Then-Optimize (PtO) paradigm maximizes decision quality by training parametric prediction models end-to-end with subsequent constrained optimization. This paper extends PtO to handle the optimization of nondifferentiable Ordered Weighted Averaging (OWA) objectives, known for their ability to ensure fair and robust solutions with respect to multiple objectives. By proposing efficient differentiable approximations of OWA optimization, it provides a framework for integrating fair optimization concepts with parametric prediction under uncertainty.