When is Majority Vote optimal? Study precisely characterizes crowdsourced label aggregation
burkov · x · 2026-10-18
Andrey Burkov summarizes research on a practical crowdsourcing question: when is Majority Vote (MV)—the simplest way to combine noisy human labels—as good as the best possible aggregation? The paper focuses on binary labeling tasks and compares MV against oracle MAP (oMAP), the method that would be optimal if true worker error rates and class distribution were known. Its objective is to characterize exactly when MV recovers the same label as oMAP for each individual task, not just on average, and whether those conditions can be checked in practice—relevant to teams spending heavily on aggregation, expert review, or gold labels.
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