Know What Not To Know: Users' Perception of Abstaining Classifiers
Abstract
Machine learning systems can help humans to make decisions by providing decision suggestions (i.e., a label for a datapoint). However, individual datapoints do not always provide enough clear evidence to make confident suggestions. Although methods exist that enable systems to identify those datapoints and subsequently abstain from suggesting a label, it remains unclear how users would react to such system behavior. This paper presents first findings from a user study on systems that do or do not abstain from labeling ambiguous datapoints. Our results show that label suggestions on ambiguous datapoints bear a high risk of unconsciously influencing the users' decisions, even toward incorrect ones. Furthermore, participants perceived a system that abstains from labeling uncertain datapoints as equally competent and trustworthy as a system that delivers label suggestions for all datapoints. Consequently, if abstaining does not impair a system's credibility, it can be a useful mechanism to increase decision quality.
Cite
@article{arxiv.2309.05443,
title = {Know What Not To Know: Users' Perception of Abstaining Classifiers},
author = {Andrea Papenmeier and Daniel Hienert and Yvonne Kammerer and Christin Seifert and Dagmar Kern},
journal= {arXiv preprint arXiv:2309.05443},
year = {2023}
}