This paper presents a novel design of the system aimed at supporting the
Wikipedia community in addressing vandalism on the platform. To achieve this,
we collected a massive dataset of 47 languages, and applied advanced filtering
and feature engineering techniques, including multilingual masked language
modeling to build the training dataset from human-generated data. The
performance of the system was evaluated through comparison with the one used in
production in Wikipedia, known as ORES. Our research results in a significant
increase in the number of languages covered, making Wikipedia patrolling more
efficient to a wider range of communities. Furthermore, our model outperforms
ORES, ensuring that the results provided are not only more accurate but also
less biased against certain groups of contributors.

本文介绍了一种新型系统的设计，旨在支持维基百科社区解决平台上的破坏行为。通过收集 47 种语言的大规模数据集和应用高级过滤和特征工程技术，包括多语言掩蔽语言模型，从人类生成的数据中构建训练数据集。通过与维基百科生产中使用的 ORES 进行比较评估系统的性能。我们的研究结果大大增加了覆盖的语言数量，使维基百科的巡逻对更广泛的社区更加高效。此外，我们的模型胜过 ORES，确保所提供的结果不仅更准确，而且对某些贡献者群体的偏见较小。

维基百科公平的多语言破坏检测系统

Fair multilingual vandalism detection system for Wikipedia

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
how quality control should work 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 several activities that have typically all been
performed by engineers: choosing or curating training data, building models to
serve predictions, auditing 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 algorithms 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 5 years since its
deployment.

本文介绍了 ORES：一种算法评分服务，支持在不同数据集上训练的多个独立分类器对 wiki 编辑进行实时评分，从而解耦了通常由工程师执行的许多活动。ORES 的设计旨在将关于维基百科算法的社会技术对话开放给更广泛的参与者。文中还讨论了 ORES 启用的社会变革机制的理论机制，并详细介绍了自其部署以来 5 年来围绕 ORES 的参与式机器学习案例研究。