效率与公平的平衡：考虑群体公平性关切的拍卖设计
Balancing Efficiency with Equality: Auction Design with Group Fairness Concerns
AI总结：
本文针对拍卖中的群体歧视问题，提出群体公平性和个体公平性两种概念，并设计群体概率机制和群体评分机制，在保持激励相容的同时实现公平与卖方收益的平衡。
摘要：
人工智能中的公平性问题源于工作推荐和风险评估等应用中的歧视性做法，这凸显了对不基于群体特征进行歧视的算法的需求。这一关切同样适用于常用于资源分配的拍卖，拍卖也需要考虑公平性。我们的研究考察了由特定属性区分的群体的拍卖，旨在（1）定义一种确保所有人得到公平对待的公平性概念，（2）识别在保持激励相容性的同时遵守这一公平性的机制，以及（3）探索公平性与卖方收益之间的平衡。我们引入了两种公平性概念——群体公平性和个体公平性——并提出了两种相应的拍卖机制：群体概率机制（Group Probability Mechanism），它满足群体公平性和激励标准；以及群体评分机制（Group Score Mechanism），它同时包含个体公平性。通过实验，我们验证了这些机制在促进公平性方面的有效性，并考察了它们对卖方收益的影响。
英文摘要：
The issue of fairness in AI arises from discriminatory practices in applications like job recommendations and risk assessments, emphasising the need for algorithms that do not discriminate based on group characteristics. This concern is also pertinent to auctions, commonly used for resource allocation, which necessitate fairness considerations. Our study examines auctions with groups distinguished by specific attributes, seeking to (1) define a fairness notion that ensures equitable treatment for all, (2) identify mechanisms that adhere to this fairness while preserving incentive compatibility, and (3) explore the balance between fairness and seller's revenue. We introduce two fairness notions-group fairness and individual fairness-and propose two corresponding auction mechanisms: the Group Probability Mechanism, which meets group fairness and incentive criteria, and the Group Score Mechanism, which also encompasses individual fairness. Through experiments, we validate these mechanisms' effectiveness in promoting fairness and examine their implications for seller revenue.
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。
