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The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels
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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.
Forward citations
Cited by 2 Pith papers
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Wisdom of the Crowd, Without the Crowd: A Socratic LLM for Asynchronous Deliberation on Perspectivist Data
A Socratic LLM that questions annotators during labeling improved post-deliberation accuracy and confidence compared to a prior synchronous human-deliberation benchmark.
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From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models
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