Documenting Data Production Processes: A Participatory Approach for Data Work

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Datum
2022
Herausgeber:innen
Autor:innen
Miceli, Milagros
Yang, Tianling
Alvarado Garcia, Adriana
Posada, Julian
Wang, Sonja Mei
Pohl,Marc
Hanna, Alex
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Zusammenfassung

The opacity of machine learning data is a significant threat to ethical data work and intelligible systems. Previous research has addressed this issue by proposing standardized checklists to document datasets. This paper expands that field of inquiry by proposing a shift of perspective: from documenting datasets towards documenting data production. We draw on participatory design and collaborate with data workers at two companies located in Bulgaria and Argentina, where the collection and annotation of data for machine learning are outsourced. Our investigation comprises 2.5 years of research, including 33 semi-structured interviews, five co-design workshops, the development of prototypes, and several feedback instances with participants. We identify key challenges and requirements related to the integration of documentation practices in real-world data production scenarios. Our findings comprise important design considerations and highlight the value of designing data documentation based on the needs of data workers. We argue that a view of documentation as a boundary object, i.e., an object that can be used differently across organizations and teams but holds enough immutable content to maintain integrity, can be useful when designing documentation to retrieve heterogeneous, often distributed, contexts of data production.

Beschreibung
Schlagwörter
dataset documentation \ data production \ data work \ data labeling \ data annotation \ machine learning \ transparency
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Zitierform
Miceli, M., Yang, T., Alvarado Garcia, A., Posada, J., Wang, S. M., Pohl, M., & Hanna, A. (2022). Documenting Data Production Processes: A Participatory Approach for Data Work. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2), 1–34. https://doi.org/10.1145/3555623