Advances in Machine Learning for the Behavioral Sciences

no code implementations • 8 Nov 2019 • Tomáš Kliegr, Štěpán Bahník, Johannes Fürnkranz

The areas of machine learning and knowledge discovery in databases have considerably matured in recent years.

BIG-bench Machine Learning

A review of possible effects of cognitive biases on the interpretation of rule-based machine learning models

no code implementations • 9 Apr 2018 • Tomáš Kliegr, Štěpán Bahník, Johannes Fürnkranz

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.

BIG-bench Machine Learning Interpretable Machine Learning

On Cognitive Preferences and the Plausibility of Rule-based Models

1 code implementation • 4 Mar 2018 • Johannes Fürnkranz, Tomáš Kliegr, Heiko Paulheim

It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones.