除了打开黑匣子：调查算法系统在维基组织文化中的作用
Beyond opening up the black box: Investigating the role of algorithmic systems in Wikipedian organizational culture
摘要：
跨领域的学者和从业者越来越关注算法的透明度和不透明性，质疑嵌入自动化黑箱系统中的价值观和假设，特别是在用户生成的内容平台中。我从维基百科上的基础设施人种志报告，讨论这个主题的一个经常未被充分研究的方面：参与高度自动化的社会技术环境的本地，上下文，学习的专业知识。今天，维基百科的组织文化与各种数据驱动的算法系统紧密相连，维基百科依靠这些系统来帮助管理和治理“任何人都可以编辑”的大规模百科全书。这些机器人、脚本、工具、插件和仪表板使维基百科对那些知道如何使用它们的人来说更有效率，但就像所有组织文化一样，如果新人想要充分参与，他们必须学习它们。我通过讨论两个自我民族志的小插曲来说明文化和组织专业知识是如何围绕算法代理制定的，这与我作为维基百科老手的个人经历有关。我将详细描述如何通过这些自动化基础设施来阐明治理和把关实践，并与之保持一致。在过去的15年里，维基百科的资深人士和管理员已经做出了具体的决定，以特定的方式支持管理和编辑工作流程的自动化，而不是其他方式。我使用维基百科的机器人支持的官僚机构的这些案例来讨论关键算法研究，关键数据研究，公平性，问责制和机器学习的透明度等领域的几个问题-最主要的是认为学术和实践必须超越试图“打开黑匣子”这样的系统，并检查社会文化过程，如新人社会化。
英文摘要：
Scholars and practitioners across domains are increasingly concerned with algorithmic transparency and opacity, interrogating the values and assumptions embedded in automated, black-boxed systems, particularly in user-generated content platforms. I report from an ethnography of infrastructure in Wikipedia to discuss an often understudied aspect of this topic: the local, contextual, learned expertise involved in participating in a highly automated social-technical environment. Today, the organizational culture of Wikipedia is deeply intertwined with various data-driven algorithmic systems, which Wikipedians rely on to help manage and govern the "anyone can edit" encyclopedia at a massive scale. These bots, scripts, tools, plugins, and dashboards make Wikipedia more efficient for those who know how to work with them, but like all organizational culture, newcomers must learn them if they want to fully participate. I illustrate how cultural and organizational expertise is enacted around algorithmic agents by discussing two autoethnographic vignettes, which relate my personal experience as a veteran in Wikipedia. I present thick descriptions of how governance and gatekeeping practices are articulated through and in alignment with these automated infrastructures. Over the past 15 years, Wikipedian veterans and administrators have made specific decisions to support administrative and editorial workflows with automation in particular ways and not others. I use these cases of Wikipedia's bot-supported bureaucracy to discuss several issues in the fields of critical algorithms studies, critical data studies, and fairness, accountability, and transparency in machine learning -- most principally arguing that scholarship and practice must go beyond trying to "open up the black box" of such systems and also examine sociocultural processes like newcomer socialization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
