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

TL;DR

This paper examines ethical issues in dataset annotation, focusing on annotator identities and platform relationships, and offers recommendations for more responsible ML dataset development.

Contribution

It provides a comprehensive survey of ethical considerations in crowdsourced annotation and proposes concrete guidelines for dataset creators to address these issues.

Findings

Annotator identities influence annotation outcomes.

Relationships with crowdsourcing platforms impact ethical considerations.

Recommendations for ethical dataset development across the ML pipeline.

Abstract

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 has not received nearly enough attention. In this paper, we survey an array of literature that provides insights into ethical considerations around crowdsourced dataset annotation. 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.