The increasing availability of real-time data has fueled the prevalence of algorithmic bidding (or autobidding) in online advertising markets, and has enabled online ad platforms to produce signals through machine learning techniques (i.e., ML advice) on advertisers' true perceived values for ad conversions. Previous works have studied the auction design problem while incorporating ML advice through various forms to improve total welfare of advertisers. Yet, such improvements could come at the cost of individual bidders' welfare, consequently eroding fairness of the ad platform. Motivated by this, we study how ad platforms can utilize ML advice to improve welfare guarantees and fairness on the individual bidder level in the autobidding world. We focus on a practical setting where ML advice takes the form of lower confidence bounds (or confidence intervals). We motivate a simple approach that directly sets such advice as personalized reserve prices when the platform consists of value-maximizing autobidders who are subject to return-on-ad spent (ROAS) constraints competing in multiple parallel auctions. Under parallel VCG auctions with ML advice-based reserves, we present a worst-case welfare lower-bound guarantee for individual agents, and show that platform fairness is positively correlated with ML advice quality. We also present an instance that demonstrates our welfare guarantee is tight. Further, we prove an impossibility result showing that no truthful, and possibly randomized mechanism with anonymous allocations and ML advice as personalized reserves can achieve universally better fairness guarantees than VCG when coupled with ML advice of the same quality. Finally, we extend our fairness guarantees with ML advice to generalized first price (GFP) and generalized second price (GSP) auctions.

翻译：实时数据的日益普及推动了在线广告市场中算法竞价（或自动竞价）的盛行，并使在线广告平台能够通过机器学习技术（即机器学习建议）产生关于广告商对广告转化真实感知价值的信号。以往研究探讨了在拍卖设计中融入各种形式的机器学习建议以提升广告商整体福利的问题。然而，这种改进可能以牺牲个别竞价者的福利为代价，从而损害广告平台的公平性。受此启发，我们研究广告平台如何利用机器学习建议在自动竞价环境中提升福利保障和个体竞价者层面的公平性。我们聚焦于实际场景：机器学习建议以置信下限（或置信区间）形式呈现。我们提出一种简单方法，即当平台中存在受广告支出回报率约束、参与多个并行拍卖的价值最大化自动竞价者时，直接将该建议设定为个性化保留价格。在基于机器学习建议保留价格的并行VCG拍卖中，我们为个体代理提供了最坏情况下的福利下限保障，并表明平台公平性与机器学习建议质量呈正相关。我们还通过实例证明该福利保障是紧致的。进一步地，我们证明了一个不可能性结果：任何诚实的、可能随机的、采用匿名分配和基于机器学习建议的个性化保留价格的机制，若耦合相同质量的机器学习建议，都无法实现比VCG更优的普适公平性保障。最后，我们将基于机器学习建议的公平性保障扩展至广义第一价格和广义第二价格拍卖。