The effects of algorithmic flagging on fairness: quasi-experimental evidence from Wikipedia
The effects of algorithmic flagging on fairness: quasi-experimental evidence from Wikipedia
复制标题
算法标记对公平性的影响：来自维基百科的准实验证据
DOI：
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复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Aaron L Halfaker
中科院分区：
文献类型：
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作者：
Nathan TeBlunthuis;Benjamin Mako Hill;Aaron L Halfaker
在线社区版主通常依赖于社交信号，如用户是否有帐户或个人资料页面，作为用户可能引起问题的线索。当主持人关注这些信号而忽视其他人的不当行为时，对这些线索的依赖可能会导致“过度描述”偏见。我们建议，部署算法标记系统来提高审核工作的效率，也可以通过减少对社交信号的依赖，使其他人违反规范的行为更加明显，从而使审核行动对这些用户更加公平。我们分析版主行为在维基百科介导的一个系统称为RCFilters，显示社会信号和算法的标志，并估计被标记的因果关系对版主的行动。我们发现，算法标记的编辑更经常被恢复，特别是具有积极社交信号的老牌编辑的编辑，并且标记降低了审核操作被撤销的可能性。我们的研究结果表明，算法标记系统可以提高公平性，但这种关系是复杂和偶然的。
Online community moderators often rely on social signals like whether or not a user has an account or a profile page as clues that users are likely to cause problems. Reliance on these clues may lead to "over-profiling" bias when moderators focus on these signals but overlook misbehavior by others. We propose that algorithmic flagging systems deployed to improve 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 a system called RCFilters that displays social signals and algorithmic flags and to estimate the causal effect of being flagged on moderator actions. We show that algorithmically flagged edits are reverted more often, especially edits 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 but that the relationship is complex and contingent.
开放式协作中的隐私、匿名和感知风险：服务提供商研究
DOI：
10.1145/3290605.3300901
发表时间：
2019
期刊：
Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19
影响因子：
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作者：
McDonald, Nora;Hill, Benjamin Mako;Greenstadt, Rachel;Forte, Andrea
通讯作者：
Forte, Andrea
DOI：
10.1145/3334480.3382960
发表时间：
2020
期刊：
CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems
影响因子：
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作者：
Kiene, Charles;Hill, Benjamin Mako
通讯作者：
Hill, Benjamin Mako
要求账户的隐性成本：来自同行生产的准实验证据
影响因子：
6.2
作者：
Hill, Benjamin Mako;Shaw, Aaron
通讯作者：
Shaw, Aaron