Evaluating the Utility of Conformal Prediction Sets for AI-Advised Image Labeling

Dongping Zhang, Angelos Chatzimparmpas, Negar Kamali, Jessica Hullman

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

As deep neural networks are more commonly deployed in high-stakes domains, their black-box nature makes uncertainty quantification challenging. We investigate the effects of presenting conformal prediction sets—a distribution-free class of methods for generating prediction sets with specified coverage—to express uncertainty in AI-advised decision-making. Through a large online experiment, we compare the utility of conformal prediction sets to displays of Top-1 and Top-k predictions for AI-advised image labeling. In a pre-registered analysis, we find that the utility of prediction sets for accuracy varies with the difficulty of the task: while they result in accuracy on par with or less than Top-1 and Top-k displays for easy images, prediction sets excel at assisting humans in labeling out-of-distribution (OOD) images, especially when the set size is small. Our results empirically pinpoint practical challenges of conformal prediction sets and provide implications on how to incorporate them for real-world decision-making.
Original languageEnglish
Title of host publicationCHI 2024 - Proceedings of the 2024 CHI Conference on Human Factors in Computing Sytems
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery
ISBN (Print)9798400703300
DOIs
Publication statusPublished - 2024
Externally publishedYes

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Bibliographical note

Publisher Copyright:
© 2024 Copyright held by the owner/author(s)

Keywords

  • comparative user experiment
  • conformal prediction
  • image labeling
  • semi-supervised learning

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