  In human-in-the-loop machine learning, the user provides information beyond
that in the training data. Many algorithms and user interfaces have been
designed 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 or
its statistics. The design of the system is then critical, as this can lead to
double use of data and overfitting, if the user reinforces noisy patterns in
the data. We propose a user modelling methodology, by assuming simple rational
behaviour, to correct the problem. We show, in a user study with 48
participants, that the method improves predictive performance in a sparse
linear regression sentiment analysis task, where graded user knowledge on
feature relevance is elicited. We believe that the key idea of inferring user
knowledge with probabilistic user models has general applicability in guarding
against overfitting and improving interactive machine learning.
