社会技术审计：拓宽算法审计视角以调查定向广告
Sociotechnical Audits: Broadening the Algorithm Auditing Lens to Investigate Targeted Advertising
AI总结：
针对算法审计缺乏社会技术视角的问题，提出社会技术审计方法及平台Intervenr，通过干预用户广告交换实验发现定向广告虽有效，但用户一周内即适应，质疑个性化定向的首要地位。
摘要：
算法审计是研究黑盒系统的有力工具。虽然算法审计在检验技术组件方面非常有效，但该方法止步于社会技术框架，该框架还会将用户视为系统中不可或缺且动态的部分。针对这一空白，我们提出了社会技术审计的概念：即在“社会技术”层面评估算法系统的审计方法，重点关注算法与用户之间的相互作用，因为两者相互影响。正如算法审计通过不同的输入探测算法并观察输出，社会技术审计（STA）额外探测用户，将用户暴露于不同的算法行为之下，并测量由此产生的态度与行为。为实例化该方法，我们开发了Intervenr平台，用于在征得参与者同意并给予补偿的情况下，开展基于浏览器的纵向社会技术审计。Intervenr调查用户在线遇到的算法内容，并协调系统性的客户端干预，以了解用户如何随之变化。作为案例研究，我们部署Intervenr进行了一项为期两周的在线广告社会技术审计（N=244），以调查“个性化广告定向对用户更有效”这一核心前提。第一周，我们收集所有推送给用户的浏览器广告；第二周，我们实施一种消融式干预，通过随机配对参与者并交换其所有广告来扰乱正常定向。我们全程收集面向用户的指标（自我报告的广告兴趣和代表性感受）和面向广告商的指标（广告浏览、点击和识别），以及总计超过50万条广告。我们的STA发现，定向广告确实对用户更有效，但用户也仅在一周内就开始适应不同的广告，这鉴于重复曝光的影响，对个性化广告定向的首要地位提出了质疑。
英文摘要：
Algorithm audits are powerful tools for studying black-box systems. While very effective in examining technical components, the method stops short of a sociotechnical frame, which would also consider users as an integral and dynamic part of the system. Addressing this gap, we propose the concept of sociotechnical auditing: auditing methods that evaluate algorithmic systems at the sociotechnical level, focusing on the interplay between algorithms and users as each impacts the other. Just as algorithm audits probe an algorithm with varied inputs and observe outputs, a sociotechnical audit (STA) additionally probes users, exposing them to different algorithmic behavior and measuring resulting attitudes and behaviors. To instantiate this method, we develop Intervenr, a platform for conducting browser-based, longitudinal sociotechnical audits with consenting, compensated participants. Intervenr investigates the algorithmic content users encounter online and coordinates systematic client-side interventions to understand how users change in response. As a case study, we deploy Intervenr in a two-week sociotechnical audit of online advertising (N=244) to investigate the central premise that personalized ad targeting is more effective on users. In the first week, we collect all browser ads delivered to users, and in the second, we deploy an ablation-style intervention that disrupts normal targeting by randomly pairing participants and swapping all their ads. We collect user-oriented metrics (self-reported ad interest and feeling of representation) and advertiser-oriented metrics (ad views, clicks, and recognition) throughout, along with a total of over 500,000 ads. Our STA finds that targeted ads indeed perform better with users, but also that users begin to acclimate to different ads in only a week, casting doubt on the primacy of personalized ad targeting given the impact of repeated exposure.
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