  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
toward 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.
