Things Machine Learning Models Know That They Don't Know
Abstract
This paper surveys Machine Learning approaches to build predictive models that know what they don't know. The consequential action of this knowledge can consist of abstaining from providing an output (rejection), deferring to another model (dynamic model selection), deferring to a human expert (learning to defer), or informing the user (uncertainty estimation). We formally state the problems each approach solves and point to key references. We discuss open issues that deserve investigation from the scientific community.
Cite
Text
Ruggieri and Pugnana. "Things Machine Learning Models Know That They Don't Know." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I27.35094Markdown
[Ruggieri and Pugnana. "Things Machine Learning Models Know That They Don't Know." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/ruggieri2025aaai-things/) doi:10.1609/AAAI.V39I27.35094BibTeX
@inproceedings{ruggieri2025aaai-things,
title = {{Things Machine Learning Models Know That They Don't Know}},
author = {Ruggieri, Salvatore and Pugnana, Andrea},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2025},
pages = {28684-28693},
doi = {10.1609/AAAI.V39I27.35094},
url = {https://mlanthology.org/aaai/2025/ruggieri2025aaai-things/}
}