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Real-world data for classification is often labeled by multiple annotators.For analyzing such data, we introduce CROWDLAB, a straightforward approach toutilize any trained classifier to estimate: (1) A consensus label for eachexample that aggregates the available annotations; (2) A confidence score forhow likely each consensus label is correct; (3) A rating for each annotatorquantifying the overall correctness of their labels. Existing algorithms toestimate related quantities in crowdsourcing often rely on sophisticatedgenerative models with iterative inference. CROWDLAB instead uses astraightforward weighted ensemble. Existing algorithms often rely solely onannotator statistics, ignoring the features of the examples from which theannotations derive. CROWDLAB utilizes any classifier model trained on thesefeatures, and can thus better generalize between examples with similarfeatures. On real-world multi-annotator image data, our proposed methodprovides superior estimates for (1)-(3) than existing algorithms likeDawid-Skene/GLAD.

CROWDLAB: Supervised learning to infer consensus labels and quality scores for data with multiple annotators