Exploring User Perceptions of Discrimination in Online Targeted Advertising
Exploring User Perceptions of Discrimination in Online Targeted Advertising
复制标题
探索用户对在线定向广告中歧视的看法
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Michael Carl Tschantz
中科院分区：
文献类型：
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作者：
Angelisa C. Plane;Elissa M. Redmiles;Michelle L. Mazurek;Michael Carl Tschantz
有针对性的在线广告目前在广告市场上占据了最大的份额，超过了电视和平面广告。有针对性的广告可以改善用户的在线购物体验，但也有负面影响。最近的大量研究发现，有证据表明，在某些情况下，广告可能具有歧视性，导致某些用户群体根据性别等个人特征看到更好的广告（例如，招聘广告）。为了制定广告政策并指导广告商做出道德决策，我们必须更好地了解用户关心的是什么以及为什么。为了回答这个问题，我们进行了一项试点研究和一项多步骤的主要调查（n = 2086），向用户展示了不同的歧视性广告场景。我们发现，总体而言，44%的受访者对我们提出的情景表示中度或非常关注。受访者发现，当歧视是由于明确的人口目标而不是对网络行为的回应而发生时，这种情况的问题要大得多。然而，我们的受访者的意见并没有因为是人类还是算法对歧视负责而有所不同。这些发现表明，未来的政策文件应明确解决定向广告中的歧视问题，无论其来源如何，这是一个重要的用户问题，而企业的回应归咎于广告生态系统的算法性质，可能无助于解决公众的担忧。
Targeted online advertising now accounts for the largest share of the advertising market, beating out both TV and print ads. While targeted advertising can improve users’ online shopping experiences, it can also have negative e ff ects. A plethora of recent work has found evidence that in some cases, ads may be discriminatory, leading certain groups of users to see better o ff ers (e.g., job ads) based on personal characteristics such as gender. To develop policies around advertising and guide advertisers in making ethical decisions, one thing we must better understand is what concerns users and why. In an e ff ort to answer this question, we conducted a pilot study and a multi-step main survey (n = 2,086 in total) presenting users with different discriminatory advertising scenarios. We find that overall, 44% of respondents were moderately or very concerned by the scenarios we presented. Respondents found the scenarios significantly more problematic when discrimination took place as a result of explicit demographic targeting rather than in response to online behavior. However, our respondents’ opinions did not vary based on whether a human or an algorithm was responsible for the discrimination. These findings suggest that future policy documents should explicitly address discrimination in targeted advertising, no matter its origin, as a significant user concern, and that corporate responses that blame the algorithmic nature of the ad ecosystem may not be helpful for addressing public concerns.