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 language | English |
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Title of host publication | CHI 2024 - Proceedings of the 2024 CHI Conference on Human Factors in Computing Sytems |
Place of Publication | New York, NY, USA |
Publisher | Association for Computing Machinery |
ISBN (Print) | 9798400703300 |
DOIs | |
Publication status | Published - 2024 |
Externally published | Yes |
Publication series
Name | Conference on Human Factors in Computing Systems - Proceedings |
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Bibliographical note
Publisher Copyright:© 2024 Copyright held by the owner/author(s)
Keywords
- comparative user experiment
- conformal prediction
- image labeling
- semi-supervised learning