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.

翻译：我们识别并探索了预测算法多群体公平性近期文献与泄露鲁棒性和图正则性这两种伪随机性概念之间的联系。我们利用基于统计距离的多校准新变体来构建研究框架，这些变体与结果不可区分性概念紧密相关。采用这一视角不仅自然地导向了我们的图论成果，还催生了特定参数区间内更高效的新多校准算法，以及实值函数硬核引理的新颖证明。