Evaluating Interpretability in Machine Teaching

• 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 settingthat involves a human domain expert as a teacher in relation to a machine learning agent. By using a prototype in a study, we discuss theinterpretability denition and show how interpretability can be evaluatedon a functional-, human- and application level. We end the paperby discussing open questions and suggestions on how our results can be transferable to other domains.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Människa-datorinteraktion (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Human Computer Interaction (hsv//eng)

Nyckelord

Interaktionsdesign

Interaktionsdesign

Publikations- och innehållstyp

vet (ämneskategori)

kon (ämneskategori)