Online advertising platforms have commonly focused on maximizing total advertiser value (or welfare) to attract advertiser traffic and spend, and have resorted to machine learning predictions on advertiser values (also known as machine-learned advice) to improve ad auction designs and thus total welfare of advertisers. Yet, such improvements could come at the cost of individual bidders' welfare, consequently eroding fairness of ad platforms, and do not shed light on how particular advertiser bidding strategies impact individual fairness. Motivated by this, we present a novel fairness metric that measures an individual bidder's welfare loss, and also uncovers how advertiser strategies relate to such losses. Under this metric, we then study how ad platforms can utilize ML advice to improve welfare guarantees and fairness on the individual bidder level. We first motivate a simple approach that directly sets such ML advice as personalized reserve prices when the platform consists of \textit{autobidders} who maximize value while respecting a return-on-ad spent (ROAS) constraint. Under parallel VCG auctions with such ML advice-based reserves, we utilize our fairness metric to present a worst-case welfare lower-bound guarantee for an individual autobidder, and show that platform fairness is positively correlated with ML advice quality as well the scale of bids induced by the autobidder's bidding strategies. Further, we prove an impossibility result showing that no truthful, and possibly randomized mechanism with anonymous allocations can achieve universally better fairness guarantees than VCG, in presence of personalized reserves based on ML-advice of equal quality. Finally, we extend our fairness guarantees to generalized first price (GFP) and generalized second price (GSP) auctions.

翻译：在线广告平台通常专注于最大化广告主总价值（或福利），以吸引广告主流量和支出，并借助机器学习预测广告主价值（即机器学习建议）来改进广告拍卖设计，从而提升广告主整体福利。然而，此类改进可能以个体投标人的福利为代价，进而侵蚀广告平台的公平性，且未能揭示特定广告主竞价策略如何影响个体公平。受此启发，我们提出一种新的公平性指标，用于衡量个体投标人的福利损失，并揭示广告主策略与这类损失之间的关联。基于该指标，我们进一步研究广告平台如何利用机器学习建议在个体投标人层面改善福利保障和公平性。首先，我们提出一种简单方法：当平台由在满足广告支出回报率（ROAS）约束下最大化价值的“自动竞价者”构成时，直接将此类机器学习建议设定为个性化保留价。在采用基于机器学习建议的保留价的并行VCG拍卖中，我们利用公平性指标为个体自动竞价者提供最坏情况下的福利下限保障，并证明平台公平性与机器学习建议质量及自动竞价者竞价策略引发的出价规模呈正相关。此外，我们证明一个不可能性结果：在基于同等质量机器学习建议的个性化保留价下，任何真实的（且可能随机化的）匿名分配机制均无法实现比VCG更普遍的公平性保障。最后，我们将公平性保障推广至广义第一价格（GFP）和广义第二价格（GSP）拍卖。