论文标题
将社区保持循环：了解基于机器学习的系统的Wikipedia利益相关者价值观
Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systems
论文作者
论文摘要
在Wikipedia上，使用复杂的算法工具用于评估编辑的质量并采取纠正措施。但是，如果算法与使用它们的社区的价值冲突，则无法解决它们为其设计的问题。在这项研究中，我们采用一种价值敏感的算法设计方法来理解一种社区创建且基于机器学习的算法，称为客观修订评估系统（ORES）---在许多Wikipedia应用程序和环境中使用的质量预测系统。利益相关者群体汇聚的五个主要价值（及其依赖的应用程序）应：（1）减少社区维护的努力，（2）保持人类作为最终权威的判断，（3）支持不同人民的不同工作流程，（4）鼓励积极与多样化的编辑团体进行积极的参与，以及（5）在社区中建立对人们和社区的信任度。我们揭示了这些价值观之间的紧张关系，并讨论了对未来研究的含义，以改善诸如矿石之类的算法。
On Wikipedia, sophisticated algorithmic tools are used to assess the quality of edits and take corrective actions. However, algorithms can fail to solve the problems they were designed for if they conflict with the values of communities who use them. In this study, we take a Value-Sensitive Algorithm Design approach to understanding a community-created and -maintained machine learning-based algorithm called the Objective Revision Evaluation System (ORES)---a quality prediction system used in numerous Wikipedia applications and contexts. Five major values converged across stakeholder groups that ORES (and its dependent applications) should: (1) reduce the effort of community maintenance, (2) maintain human judgement as the final authority, (3) support differing peoples' differing workflows, (4) encourage positive engagement with diverse editor groups, and (5) establish trustworthiness of people and algorithms within the community. We reveal tensions between these values and discuss implications for future research to improve algorithms like ORES.
