算法标记对公平性的影响：来自维基百科的准实验证据
Effects of algorithmic flagging on fairness: quasi-experimental evidence from Wikipedia
AI总结：
本文研究了算法标记对维基百科管理员公平性的影响，发现算法标记可减少对社会信号的依赖，提高审核公平性，但效果复杂且依赖具体情境。
摘要：
在线社区管理员往往依赖诸如用户是否有账号或资料页等社会信号作为用户可能造成问题的线索。依赖这些线索可能导致“过度标记”偏见，当管理员专注于这些信号但忽视他人的不当行为时。我们提出，旨在提高审核效率的算法标记系统也可以通过减少对社会信号的依赖并使其他人违反规范的行为更明显，使审核行动对这些用户更加公平。我们分析了维基百科中由RCFilters系统中介的管理员行为，并估计被标记对管理员行动的因果影响。我们显示，算法标记的编辑被撤销的频率更高，尤其是那些由有积极社会信号的资深编辑所进行的编辑，且标记会降低审核行动被撤销的可能性。我们的结果表明，算法标记系统在某些情况下可能导致公平性增加，但这种关系是复杂且依赖性的。
英文摘要：
Online community moderators often rely on social signals such as whether or not a user has an account or a profile page as clues that users may cause problems. Reliance on these clues can lead to "overprofiling'' bias when moderators focus on these signals but overlook the misbehavior of others. We propose that algorithmic flagging systems deployed to improve the efficiency of moderation work can also make moderation actions more fair to these users by reducing reliance on social signals and making norm violations by everyone else more visible. We analyze moderator behavior in Wikipedia as mediated by RCFilters, a system which displays social signals and algorithmic flags, and estimate the causal effect of being flagged on moderator actions. We show that algorithmically flagged edits are reverted more often, especially those by established editors with positive social signals, and that flagging decreases the likelihood that moderation actions will be undone. Our results suggest that algorithmic flagging systems can lead to increased fairness in some contexts but that the relationship is complex and contingent.
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