ORES: Facilitating re-mediation of Wikipedia’s socio-technical problems
ORES: Facilitating re-mediation of Wikipedia’s socio-technical problems
复制标题
ORES：促进维基百科社会技术问题的修复
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
R. Geiger
中科院分区：
文献类型：
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作者：
Aaron L Halfaker;R. Geiger
从基于规则的机器人到机器学习分类器的算法系统已经有一个悠久的历史来支持内容中的基本工作和同行生产项目中的其他策划工作。像Wikipedia可以扩展到世界上最大的百科全书，同时保持质量和一致性。在本文中修改这些复杂的算法系统。或策划培训模型，以提供预测，并在这些预测上开发界面或自动化的代理本文，我们讨论了社会变革的理论机制，可以在部署以来的三年内参与机器学习中的详细案例研究。
Algorithmic systems—from rule-based bots to machine learning classifiers—have a long history of supporting the essential work of content moderation and other curation work in peer production projects. From counter-vandalism to task routing, basic machine prediction has allowed open knowledge projects like Wikipedia to scale to the largest encyclopedia in the world, while maintaining quality and consistency. However, conversations about what quality control should be and what role algorithms should play have generally been led by the expert engineers who have the skills and resources to develop and modify these complex algorithmic systems. In this paper, we describe ORES: an algorithmic scoring service that supports real-time scoring of wiki edits using multiple independent classifiers trained on different datasets. ORES decouples three activities that have typically all been performed by engineers: choosing or curating training data, building models to serve predictions, and developing interfaces or automated agents that act on those predictions. This meta-algorithmic system was designed to open up socio-technical conversations about algorithmic systems in Wikipedia to a broader set of participants. In this paper, we discuss the theoretical mechanisms of social change ORES enables and detail case studies in participatory machine learning around ORES from the 3 years since its deployment.