Over the past decade, considerable research and industry efforts have focused on addressing biases and minimizing personal subjectivities in data collection, curation, classification, and labeling by data workers. In this talk, I propose a shift of perspective to explore power imbalances inherent in data work that significantly shape datasets and systems. This means accounting for historical inequities, labor conditions, and epistemological standpoints. Starting from the assumption that power imbalances are the problem, not just bias, leads to fundamentally different research questions and methods of inquiry. In this sense, I highlight the need for centering labor as a fundamental dimension in the field of “AI” ethics as well as fostering interdisciplinary dialogue and cooperation in the study of data quality.