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Human annotated data plays a crucial role in machine learning (ML) researchand development. However, the ethical considerations around the processes anddecisions that go into dataset annotation have 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 introduce a novel framework, CrowdWorkSheets, for dataset developers tofacilitate transparent documentation of key decisions points at various stagesof the data annotation pipeline: task formulation, selection of annotators,platform and infrastructure choices, dataset analysis and evaluation, anddataset release and maintenance.

CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation