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In human-in-the-loop machine learning, the user provides information beyondthat in the training data. Many algorithms and user interfaces have beendesigned to optimize and facilitate this human--machine interaction; however,fewer studies have addressed the potential defects the designs can cause.Effective interaction often requires exposing the user to the training data orits statistics. The design of the system is then critical, as this can lead todouble use of data and overfitting, if the user reinforces noisy patterns inthe data. We propose a user modelling methodology, by assuming simple rationalbehaviour, to correct the problem. We show, in a user study with 48participants, that the method improves predictive performance in a sparselinear regression sentiment analysis task, where graded user knowledge onfeature relevance is elicited. We believe that the key idea of inferring userknowledge with probabilistic user models has general applicability in guardingagainst overfitting and improving interactive machine learning.

User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction