Metric Elicitation; Moving from Theory to Practice
Abstract
Metric Elicitation (ME) is a framework for eliciting classification metrics that better align with implicit user preferences based on the task and context. The existing ME strategy so far is based on the assumption that users can most easily provide preference feedback over classifier statistics such as confusion matrices. This work examines ME, by providing a first ever implementation of the ME strategy. Specifically, we create a web-based ME interface and conduct a user study that elicits users' preferred metrics in a binary classification setting. We discuss the study findings and present guidelines for future research in this direction.
Cite
@article{arxiv.2212.03495,
title = {Metric Elicitation; Moving from Theory to Practice},
author = {Safinah Ali and Sohini Upadhyay and Gaurush Hiranandani and Elena L. Glassman and Oluwasanmi Koyejo},
journal= {arXiv preprint arXiv:2212.03495},
year = {2022}
}
Comments
The paper to appear at Human-Centered AI workshop at NeurIPS, 2022. arXiv admin note: text overlap with arXiv:2208.09142