  An important factor to guarantee a fair use of data-driven recommendation
systems is that we should be able to communicate their uncertainty to decision
makers. This can be accomplished by constructing prediction intervals, which
provide an intuitive measure of the limits of predictive performance. To
support equitable treatment, we force the construction of such intervals to be
unbiased in the sense that their coverage must be equal across all protected
groups of interest. We present an operational methodology that achieves this
goal by offering rigorous distribution-free coverage guarantees holding in
finite samples. Our methodology, equalized coverage, is flexible as it can be
viewed as a wrapper around any predictive algorithm. We test the applicability
of the proposed framework on real data, demonstrating that equalized coverage
constructs unbiased prediction intervals, unlike competitive methods.
