[论文解读] Discrimination through Optimization: How Facebook's Ad Delivery Can Lead to Biased Outcomes

Muhammad Ali, Piotr Sapieżyński+4 more|arXiv (Cornell University)|Nov 7, 2019

Consumer Market Behavior and Pricing参考文献 33被引用 116

一句话总结

本文表明，即使广告商使用中性定位，Facebook 的广告投放算法仍可能因平台层面的关联性与广告表现优化而沿性别和种族维度产生歧视性结果。研究发现，预算分配与广告内容显著扭曲了投放效果，导致住房与就业广告在可见性上出现不平等，尽管定位设置为包容性。

ABSTRACT

The enormous financial success of online advertising platforms is partially due to the precise targeting features they offer. Although researchers and journalists have found many ways that advertisers can target---or exclude---particular groups of users seeing their ads, comparatively little attention has been paid to the implications of the platform's ad delivery process, comprised of the platform's choices about which users see which ads. It has been hypothesized that this process can ad delivery in ways that the advertisers do not intend, making some users less likely than others to see particular ads based on their demographic characteristics. In this paper, we demonstrate that such skewed delivery occurs on Facebook, due to market and financial optimization effects as well as the platform's own predictions about the relevance of ads to different groups of users. We find that both the advertiser's budget and the content of the ad each significantly contribute to the skew of Facebook's ad delivery. Critically, we observe significant skew in delivery along gender and racial lines for real ads for employment and housing opportunities despite neutral targeting parameters. Our results demonstrate previously unknown mechanisms that can lead to potentially discriminatory ad delivery, even when advertisers set their targeting parameters to be highly inclusive. This underscores the need for policymakers and platforms to carefully consider the role of the ad delivery optimization run by ad platforms themselves---and not just the targeting choices of advertisers---in preventing discrimination in digital advertising.

研究动机与目标

• 调查 Facebook 的广告投放系统是否可能在广告商不主动选择特定目标群体的情况下，仍产生歧视性结果。
• 研究平台层面的优化机制（如关联性预测与经济激励）如何影响不同人口群体的广告可见性。
• 评估广告内容与预算对就业与住房广告投放偏差的影响。
• 强调平台投放算法作为数字广告中此前被低估的系统性偏见来源的作用。

提出的方法

• 使用真实 Facebook 广告开展受控实验，针对住房与就业机会，采用中性定位参数。
• 在不同人口群体间调整广告内容与预算水平，以衡量投放差异。
• 利用平台报告的广告可见性与参与度指标分析投放结果。
• 比较不同性别与种族群体的投放率，以检测系统性偏差。
• 评估 Facebook 的关联性评分与优化算法对广告分发的影响。

实验结果

研究问题

• RQ1 当广告商使用中性定位时，Facebook 的广告投放系统是否仍可能产生歧视性结果？
• RQ2 广告内容与预算在多大程度上影响了不同人口群体间广告投放的偏差？
• RQ3 平台层面的优化机制（如关联性预测与经济激励）如何导致广告可见性的不平等？
• RQ4 在性别与种族维度上，就业与住房广告的投放是否存在可测量的差异？

主要发现

• 尽管采用中性定位参数，Facebook 的广告投放系统仍对就业与住房广告造成了显著的性别与种族维度的可见性偏差。
• 广告内容与预算分配被证实显著加剧了投放结果的偏差。
• 平台对关联性与表现的优化导致某些人口群体的广告可见性降低，即使广告商本意是包容性投放。
• 即使广告商未明确排除任何群体，投放偏差依然发生，表明平台的算法过程是歧视性结果的关键来源。
• 本研究揭示，平台驱动的投放机制可独立于广告商意图持续加剧偏见，凸显了数字广告生态系统中的系统性问题。