Diverse Perspectives Can Mitigate Political Bias in Crowdsourced Content Moderation
Diverse Perspectives Can Mitigate Political Bias in Crowdsourced Content Moderation
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DOI：
10.1145/3593013.3594080
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Jacob Thebault-Spieker;Sukrit Venkatagiri;Naomi Mine;Kurt Luther
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文献类型：
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作者：
Jacob Thebault-Spieker;Sukrit Venkatagiri;Naomi Mine;Kurt Luther
近年来，社交媒体公司一直在努力定义和执行围绕其平台上政治内容的内容审核政策，部分原因是担心政治偏见，虚假信息和两极分化。这些政策采取了多种形式，包括禁止政治广告，限制政治话题的范围，核查政治主张，以及允许用户完全隐藏政治内容。然而，实施这些政策需要人类的判断来标记政治内容，目前还不清楚人类标记者在这项任务中的表现如何，或者偏见是否会影响这一过程。因此，在这项研究中，我们实验性地评估了使用人群工作者识别政治内容的可行性和实用性，我们发现了使识别这些内容变得困难的偏见。我们的研究结果成问题的人群组成的看似可互换的工人，并提供了初步的证据表明，从异质性工人的综合判断可能有助于减轻政治偏见。根据这些发现，我们确定了实现更公平的标签结果的策略，同时也更好地支持人群工作者完成这项任务，并可能减轻偏见。
In recent years, social media companies have grappled with defining and enforcing content moderation policies surrounding political content on their platforms, due in part to concerns about political bias, disinformation, and polarization. These policies have taken many forms, including disallowing political advertising, limiting the reach of political topics, fact-checking political claims, and enabling users to hide political content altogether. However, implementing these policies requires human judgement to label political content, and it is unclear how well human labelers perform at this task, or whether biases affect this process. Therefore, in this study we experimentally evaluate the feasibility and practicality of using crowd workers to identify political content, and we uncover biases that make it difficult to identify this content. Our results problematize crowds composed of seemingly interchangeable workers, and provide preliminary evidence that aggregating judgements from heterogeneous workers may help mitigate political biases. In light of these findings, we identify strategies to achieving fairer labeling outcomes, while also better supporting crowd workers at this task and potentially mitigating biases.