在确保公平性的同时最大化覆盖率：一个关于冲突目标的故事
Maximizing coverage while ensuring fairness: a tale of conflicting objective
AI总结：
该研究从组合优化角度出发，构建含着色公平约束的最大覆盖问题框架，提出一组随机与确定性近似算法，以同时优化相互冲突的公平性与覆盖率目标。
摘要：
近年来，受公平资源分配和社会正义考量的推动，在计算问题中确保公平性已成为一个关键主题。我们可以从多个角度将公平性纳入计算问题，例如使用优化、博弈论或机器学习框架。在本文中，我们从组合优化的角度解决纳入公平性的问题。我们构建了一个适用于近似算法及相关领域研究人员分析的组合优化框架，该框架将公平性引入最大覆盖问题，将其视为两个相互冲突目标之间的相互作用。我们通过着色约束来实现覆盖的公平性，这类约束可最小化所选集合覆盖的不同颜色元素数量之间的差异；这与文献中广泛研究的常见差异最小化问题形成对比，后者的（通常是两种）颜色并非预先给定，而是需要通过选择来最小化每个单独集合的最大颜色差异。我们的主要结果是一组随机和确定性近似算法，它们试图在该框架下同时近似公平性和覆盖率。
英文摘要：
Ensuring fairness in computational problems has emerged as a $key$ topic during recent years, buoyed by considerations for equitable resource distributions and social justice. It $is$ possible to incorporate fairness in computational problems from several perspectives, such as using optimization, game-theoretic or machine learning frameworks. In this paper we address the problem of incorporation of fairness from a $combinatorial$ $optimization$ perspective. We formulate a combinatorial optimization framework, suitable for analysis by researchers in approximation algorithms and related areas, that incorporates fairness in maximum coverage problems as an interplay between $two$ conflicting objectives. Fairness is imposed in coverage by using coloring constraints that $minimizes$ the discrepancies between number of elements of different colors covered by selected sets; this is in contrast to the usual discrepancy minimization problems studied extensively in the literature where (usually two) colors are $not$ given $a$ $priori$ but need to be selected to minimize the maximum color discrepancy of $each$ individual set. Our main results are a set of randomized and deterministic approximation algorithms that attempts to $simultaneously$ approximate both fairness and coverage in this framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
