Toward Controlling Discrimination in Online Ad Auctions
Toward Controlling Discrimination in Online Ad Auctions
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发表时间：
2019-01
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通讯作者：
L. E. Celis;Anay Mehrotra;Nisheeth K. Vishnoi
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作者：
L. E. Celis;Anay Mehrotra;Nisheeth K. Vishnoi
由于他们为广告商提供的可定制观众，在线广告平台正在蓬勃发展。但是，最近的研究表明，广告对于看到广告的观众的性别或种族可以歧视，并且可能无意间越过道德和/或法律界限。为了防止这种情况，我们提出了一个受限制的广告拍卖框架，该框架最大化平台的收入为确保观众看到广告商广告的观众在性别或种族等敏感类型上适当分发。在Myerson的经典作品的基础上，我们首先提出了针对大量公平限制的最佳拍卖机制。但是，发现此最佳拍卖的参数被证明是一个非凸面问题。我们表明，这个非凸面问题可以重新重新构成一个更结构化的非凸问题，没有鞍点或局部 - 马克西马。这使我们能够开发一种基于梯度的算法来解决它。我们对A1 Yahoo！的经验结果数据集证明，我们的算法可以为每个广告商提供统一的覆盖范围，但对平台的收入造成了微小的损失，并且对每个广告商所达到的受众群体的大小进行了很小的变化。
Online advertising platforms are thriving due to the customizable audiences they offer advertisers. However, recent studies show that advertisements can be discriminatory with respect to the gender or race of the audience that sees the ad, and may inadvertently cross ethical and/or legal boundaries. To prevent this, we propose a constrained ad auction framework that maximizes the platform's revenue conditioned on ensuring that the audience seeing an advertiser's ad is distributed appropriately across sensitive types such as gender or race. Building upon Myerson's classic work, we first present an optimal auction mechanism for a large class of fairness constraints. Finding the parameters of this optimal auction, however, turns out to be a non-convex problem. We show that this non-convex problem can be reformulated as a more structured non-convex problem with no saddle points or local-maxima; this allows us to develop a gradient-descent-based algorithm to solve it. Our empirical results on the A1 Yahoo! dataset demonstrate that our algorithm can obtain uniform coverage across different user types for each advertiser at a minor loss to the revenue of the platform, and a small change to the size of the audience each advertiser reaches.