1 research outputs found
A review of possible effects of cognitive biases on interpretation of rule-based machine learning models
While the interpretability of machine learning models is often equated with
their mere syntactic comprehensibility, we think that interpretability goes
beyond that, and that human interpretability should also be investigated from
the point of view of cognitive science. In particular, the goal of this paper
is to discuss to what extent cognitive biases may affect human understanding of
interpretable machine learning models, in particular of logical rules
discovered from data. Twenty cognitive biases are covered, as are possible
debiasing techniques that can be adopted by designers of machine learning
algorithms and software. Our review transfers results obtained in cognitive
psychology to the domain of machine learning, aiming to bridge the current gap
between these two areas. It needs to be followed by empirical studies
specifically focused on the machine learning domain