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Human annotations play a crucial role in machine learning (ML) research anddevelopment. However, the ethical considerations around the processes anddecisions that go into building ML datasets has not received nearly enoughattention. In this paper, we survey an array of literature that providesinsights into ethical considerations around crowdsourced dataset annotation. Wesynthesize these insights, and lay out the challenges in this space along twolayers: (1) who the annotator is, and how the annotators' lived experiences canimpact their annotations, and (2) the relationship between the annotators andthe crowdsourcing platforms and what that relationship affords them. Finally,we put forth a concrete set of recommendations and considerations for datasetdevelopers at various stages of the ML data pipeline: task formulation,selection of annotators, platform and infrastructure choices, dataset analysisand evaluation, and dataset documentation and release.

Whose Ground Truth? Accounting for Individual and Collective Identities Underlying Dataset Annotation