annotator demographics

Longstanding data labeling practices in machine learning involve collecting and aggregating labels from multiple annotators. But what should we do when annotators disagree? Though annotator disagreement has long been seen as a problem to minimize, new perspectivist approaches challenge this assumption by treating disagreement as a valuable source of information. In this position paper, we examine practices and assumptions surrounding the causes of disagreement—some challenged by perspectivist approaches, and some that remain to be addressed—as well as practical and normative challenges for work operating under these assumptions. We conclude with recommendations for the data labeling pipeline and avenues for future research engaging with subjectivity and disagreement.

People often disagree on subjective tasks such as determining what is offensive or toxic online, where each annotator brings their own perspective influenced by factors like culture, identity, and lived experience. In many annotation settings today, however, we ask multiple people — who may have different beliefs — to provide just one label per example, treating majority vote as ground truth. We show how training models to capture individual annotator behavior instead yields better modeling of disagreement patterns among human raters.