  Research in machine learning (ML) has primarily argued that models trained on
incomplete or biased datasets can lead to discriminatory outputs. In this
commentary, we propose moving the research focus beyond bias-oriented framings
by adopting a power-aware perspective to "study up" ML datasets. This means
accounting for historical inequities, labor conditions, and epistemological
standpoints inscribed in data. We draw on HCI and CSCW work to support our
argument, critically analyze previous research, and point at two co-existing
lines of work within our community -- one bias-oriented, the other power-aware.
This way, we highlight the need for dialogue and cooperation in three areas:
data quality, data work, and data documentation. In the first area, we argue
that reducing societal problems to "bias" misses the context-based nature of
data. In the second one, we highlight the corporate forces and market
imperatives involved in the labor of data workers that subsequently shape ML
datasets. Finally, we propose expanding current transparency-oriented efforts
in dataset documentation to reflect the social contexts of data design and
production.
