  We identify and explore connections between the recent literature on
multi-group fairness for prediction algorithms and the pseudorandomness notions
of leakage-resilience and graph regularity. We frame our investigation using
new, statistical distance-based variants of multicalibration that are closely
related to the concept of outcome indistinguishability. Adopting this
perspective leads us naturally not only to our graph theoretic results, but
also to new, more efficient algorithms for multicalibration in certain
parameter regimes and a novel proof of a hardcore lemma for real-valued
functions.
