Discipline and Label: A WEIRD Genealogy and Social Theory of Data Annotation

• URL: http://arxiv.org/abs/2402.06811v1
• Date: Fri, 9 Feb 2024 22:21:55 GMT
• Title: Discipline and Label: A WEIRD Genealogy and Social Theory of Data Annotation
• Authors: Andrew Smart, Ding Wang, Ellis Monk, Mark D\'iaz, Atoosa Kasirzadeh, Erin Van Liemt, Sonja Schmer-Galunder
• Abstract summary: Data annotation remains the sine qua non of machine learning and AI. Recent empirical work has highlighted the importance of rater diversity for fairness, model performance, and the role of annotator subjectivity on labels. This paper outlines a critical genealogy of data annotation; starting with its psychological and perceptual aspects.
• Score: 11.48611587310938
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data annotation remains the sine qua non of machine learning and AI. Recent empirical work on data annotation has begun to highlight the importance of rater diversity for fairness, model performance, and new lines of research have begun to examine the working conditions for data annotation workers, the impacts and role of annotator subjectivity on labels, and the potential psychological harms from aspects of annotation work. This paper outlines a critical genealogy of data annotation; starting with its psychological and perceptual aspects. We draw on similarities with critiques of the rise of computerized lab-based psychological experiments in the 1970's which question whether these experiments permit the generalization of results beyond the laboratory settings within which these results are typically obtained. Do data annotations permit the generalization of results beyond the settings, or locations, in which they were obtained? Psychology is overly reliant on participants from Western, Educated, Industrialized, Rich, and Democratic societies (WEIRD). Many of the people who work as data annotation platform workers, however, are not from WEIRD countries; most data annotation workers are based in Global South countries. Social categorizations and classifications from WEIRD countries are imposed on non-WEIRD annotators through instructions and tasks, and through them, on data, which is then used to train or evaluate AI models in WEIRD countries. We synthesize evidence from several recent lines of research and argue that data annotation is a form of automated social categorization that risks entrenching outdated and static social categories that are in reality dynamic and changing. We propose a framework for understanding the interplay of the global social conditions of data annotation with the subjective phenomenological experience of data annotation work.

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