  Group-fairness in classification aims for equality of a predictive utility
across different sensitive sub-populations, e.g., race or gender. Equality or
near-equality constraints in group-fairness often worsen not only the aggregate
utility but also the utility for the least advantaged sub-population. In this
paper, we apply the principles of Pareto-efficiency and least-difference to the
utility being accuracy, as an illustrative example, and arrive at the Rawls
classifier that minimizes the error rate on the worst-off sensitive
sub-population. Our mathematical characterization shows that the Rawls
classifier uniformly applies a threshold to an ideal score of features, in the
spirit of fair equality of opportunity. In practice, such a score or a feature
representation is often computed by a black-box model that has been useful but
unfair. Our second contribution is practical Rawlsian fair adaptation of any
given black-box deep learning model, without changing the score or feature
representation it computes. Given any score function or feature representation
and only its second-order statistics on the sensitive sub-populations, we seek
a threshold classifier on the given score or a linear threshold classifier on
the given feature representation that achieves the Rawls error rate restricted
to this hypothesis class. Our technical contribution is to formulate the above
problems using ambiguous chance constraints, and to provide efficient
algorithms for Rawlsian fair adaptation, along with provable upper bounds on
the Rawls error rate. Our empirical results show significant improvement over
state-of-the-art group-fair algorithms, even without retraining for fairness.
