Probabilistic Learning to Defer: Handling Missing Expert Annotations and Controlling Workload Distribution
Keywords: learning to defer, expectation - maximisation
Abstract: Recent progress in machine learning research is gradually shifting its focus towards *human - AI cooperation* due to the advantages of exploiting the reliability of human experts and the efficiency of AI models. One of the promising approaches in human - AI cooperation is *learning to defer* (L2D), where the system analyses the input data and decides to make its own decision or defer to human experts. Although L2D has demonstrated state-of-the-art performance, in its standard setting, L2D entails a severe limitation: all human experts must annotate the whole training dataset of interest, resulting in a slow and expensive annotation process which can subsequently influence the size and diversity of the training set. Moreover, the current L2D does not have a principled way to control workload distribution among human experts and the AI classifier that is important to optimise resource allocation. We, therefore, propose a new probabilistic modelling approach inspired from mixture-of-experts, where the Expectation - Maximisation algorithm is leveraged to address the issue of missing expert's annotations. Furthermore, we introduce a constraint, which can be solved efficiently during the E-step, to control the workload distribution among human experts and the AI classifier. Empirical evaluation on synthetic and real-world datasets show that our proposed probabilistic approach performs competitively, or even surpasses previously proposed methods assessed on the same benchmarks.
Primary Area: other topics in machine learning (i.e., none of the above)
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Submission Number: 451
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