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Information systems increasingly leverage artificial intelligence (AI) andmachine learning (ML) to generate value from vast amounts of data. However, MLmodels are imperfect and can generate incorrect classifications. Hence,human-in-the-loop (HITL) extensions to ML models add a human review forinstances that are difficult to classify. This study argues that continuouslyrelying on human experts to handle difficult model classifications leads to astrong increase in human effort, which strains limited resources. To addressthis issue, we propose a hybrid system that creates artificial experts thatlearn to classify data instances from unknown classes previously reviewed byhuman experts. Our hybrid system assesses which artificial expert is suitablefor classifying an instance from an unknown class and automatically assigns it.Over time, this reduces human effort and increases the efficiency of thesystem. Our experiments demonstrate that our approach outperforms traditionalHITL systems for several benchmarks on image classification.

Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human Experts