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Scholars and practitioners across domains are increasingly concerned withalgorithmic transparency and opacity, interrogating the values and assumptionsembedded in automated, black-boxed systems, particularly in user-generatedcontent platforms. I report from an ethnography of infrastructure in Wikipediato discuss an often understudied aspect of this topic: the local, contextual,learned expertise involved in participating in a highly automatedsocial-technical environment. Today, the organizational culture of Wikipedia isdeeply intertwined with various data-driven algorithmic systems, whichWikipedians rely on to help manage and govern the "anyone can edit"encyclopedia at a massive scale. These bots, scripts, tools, plugins, anddashboards make Wikipedia more efficient for those who know how to work withthem, but like all organizational culture, newcomers must learn them if theywant to fully participate. I illustrate how cultural and organizationalexpertise is enacted around algorithmic agents by discussing twoautoethnographic vignettes, which relate my personal experience as a veteran inWikipedia. I present thick descriptions of how governance and gatekeepingpractices are articulated through and in alignment with these automatedinfrastructures. Over the past 15 years, Wikipedian veterans and administratorshave made specific decisions to support administrative and editorial workflowswith automation in particular ways and not others. I use these cases ofWikipedia's bot-supported bureaucracy to discuss several issues in the fieldsof critical algorithms studies, critical data studies, and fairness,accountability, and transparency in machine learning -- most principallyarguing that scholarship and practice must go beyond trying to "open up theblack box" of such systems and also examine sociocultural processes likenewcomer socialization.

Beyond opening up the black box: Investigating the role of algorithmic systems in Wikipedian organizational culture