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Found 5 papers, 3 papers with code
"Garbage In, Garbage Out" Revisited: What Do Machine Learning Application Papers Report About Human-Labeled Training Data?
Supervised machine learning, in which models are automatically derived from labeled training data, is only as good as the quality of that data.
Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?
Many machine learning projects for new application areas involve teams of humans who label data for a particular purpose, from hiring crowdworkers to the paper's authors labeling the data themselves.
ORES: Lowering Barriers with Participatory Machine Learning in 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.
The Lives of Bots
Automated software agents --- or bots --- have long been an important part of how Wikipedia's volunteer community of editors write, edit, update, monitor, and moderate content.
Beyond opening up the black box: Investigating the role of algorithmic systems in Wikipedian organizational culture
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.
