Evaluating interpretability in machine teaching

0Citations
Citations of this article
5Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Building interpretable machine learning agents is a challenge that needs to be addressed to make the agents trustworthy and align the usage of the technology with human values. In this work, we focus on how to evaluate interpretability in a machine teaching setting, a setting that involves a human domain expert as a teacher in relation to a machine learning agent. By using a prototype in a study, we discuss the interpretability definition and show how interpretability can be evaluated on a functional-, human- and application level. We end the paper by discussing open questions and suggestions on how our results can be transferable to other domains.

Cite

CITATION STYLE

APA

Holmberg, L., Davidsson, P., & Linde, P. (2020). Evaluating interpretability in machine teaching. In Communications in Computer and Information Science (Vol. 1233 CCIS, pp. 54–65). Springer. https://doi.org/10.1007/978-3-030-51999-5_5

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free