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Research questionHow can machine learning learn from contested labels without assuming one objectively correct target?Standard predictive modeling often treats labels as observations of a single correct target. When judgments differ across sources, disagreement may reflect meaningful perspectives rather than simple annotation error.
Evaluation & Benchmarks
Machine Learning
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.Negative Ontology of True Target for Machine Learning: Towards Recognition, Evaluation and Learning under Democratic SupervisionThe source develops Democratic Supervision and Multiple Inaccurate True Targets (MIATTs), including logic-driven target construction and assessment, evaluation formulas, and learning without a definable true target. It examines the framework in a synthetic controlled environment and reports a real-world application in individual education and professional development, with implications for continuing human-AI co-evolution.research paper · Sep 8, 2026
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