A hunt for the Snark: Annotator Diversity in Data Practices

Shivani Kapania, Alex S. Taylor, Ding Wang

Abstract: Diversity in datasets is a key component to building responsible AI/ML. Despite this recognition, we know little about the diversity among the annotators involved in data production. We investigated the approaches to annotator diversity through 16 semi-structured interviews and a survey with 44 AI/ML practitioners. While practitioners described nuanced understandings of annotator diversity, they rarely designed dataset production to account for diversity in the annotation process. The lack of action was explained through operational barriers: from the lack of visibility in the annotator hiring process, to the conceptual difficulty in incorporating worker diversity. We argue that such operational barriers and the widespread resistance to accommodating annotator diversity surface a prevailing logic in data practices—where neutrality, objectivity and ‘representationalist thinking’ dominate. By understanding this logic to be part of a regime of existence, we explore alternative ways of accounting for annotator subjectivity and diversity in data practices.