Title: Long Term Fairness via Performative Distributionally Robust Optimization. Abstract: Fairness researchers in machine learning (ML) have coalesced around several fairness criteria which provide formal definitions of what it means for an ML model to be fair. However, these criteria have some serious limitations. We identify four key shortcomings of these formal fairness criteria and address them by extending performative prediction to include a distributionally robust objective.  Performative prediction is a recent framework developed to understand the effects of when deploying model influences the distribution on which it is making predictions.  We prove a convergence result for our proposed repeated distributionally robust optimization (RDRO).  We further verify our results empirically and develop experiments to demonstrate the impact of using RDRO on learning fair ML models.