Human annotations play a crucial role in machine learning (ML) research and development. However, the ethical considerations around the processes and decisions that go into building ML datasets, essentially shaping the research trajectories within our field, has not gotten nearly enough attention. In this paper, we survey an array of literature on human computation, with a focus on ethical considerations around crowdsourcing. We synthesize these insights, and lay out the challenges in this space along two layers: (1) who the annotator is and how the annotators' lived experiences can impact their annotations, and (2) the relationship between the annotators and the crowdsourcing platforms and what that relationship affords them. Finally, we put forth a concrete set of recommendations and considerations for dataset developers at various stages of the ML data pipeline: task formulation, selection of annotators, platform and infrastructure choices, dataset analysis and evaluation, and dataset documentation and release.