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Active Learning (AL) is a human-in-the-loop framework to interactively andadaptively label data instances, thereby enabling significant gains in modelperformance compared to random sampling. AL approaches function by selectingthe hardest instances to label, often relying on notions of diversity anduncertainty. However, we believe that these current paradigms of AL do notleverage the full potential of human interaction granted by automated labelsuggestions. Indeed, we show that for many classification tasks and datasets,most people verifying if an automatically suggested label is correct take$3\times$ to $4\times$ less time than they do changing an incorrect suggestionto the correct label (or labeling from scratch without any suggestion).Utilizing this result, we propose CLARIFIER (aCtive LeARnIng From tIEredhaRdness), an Interactive Learning framework that admits more effective use ofhuman interaction by leveraging the reduced cost of verification. By targetingthe hard (uncertain) instances with existing AL methods, the intermediateinstances with a novel label suggestion scheme using submodular mutualinformation functions on a per-class basis, and the easy (confident) instanceswith highest-confidence auto-labeling, CLARIFIER can improve over theperformance of existing AL approaches on multiple datasets -- particularly onthose that have a large number of classes -- by almost 1.5$\times$ to 2$\times$in terms of relative labeling cost.

Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification