Author
Listed:
- Wenhao Zhou
(Tsinghua University
Tsinghua University
Tsinghua University
Tsinghua University)
- Faqiang Liu
(Tsinghua University
Tsinghua University
Tsinghua University
Tsinghua University)
- Hao Zheng
(Tsinghua University
Tsinghua University
Tsinghua University
Tsinghua University)
- Rong Zhao
(Tsinghua University
Tsinghua University
Tsinghua University
Tsinghua University)
Abstract
Shortcut learning poses a significant challenge to both the interpretability and robustness of artificial intelligence, arising from dataset biases that lead models to exploit unintended correlations, or shortcuts, which undermine performance evaluations. Addressing these inherent biases is particularly difficult due to the complex, high-dimensional nature of data. Here, we introduce shortcut hull learning, a diagnostic paradigm that unifies shortcut representations in probability space and utilizes diverse models with different inductive biases to efficiently learn and identify shortcuts. This paradigm establishes a comprehensive, shortcut-free evaluation framework, validated by developing a shortcut-free topological dataset to assess deep neural networks’ global capabilities, enabling a shift from Minsky and Papert’s representational analysis to an empirical investigation of learning capacity. Unexpectedly, our experimental results suggest that under this framework, convolutional models—typically considered weak in global capabilities—outperform transformer-based models, challenging prevailing beliefs. By enabling robust and bias-free evaluation, our framework uncovers the true model capabilities beyond architectural preferences, offering a foundation for advancing AI interpretability and reliability.
Suggested Citation
Wenhao Zhou & Faqiang Liu & Hao Zheng & Rong Zhao, 2025.
"Mitigating data bias and ensuring reliable evaluation of AI models with shortcut hull learning,"
Nature Communications, Nature, vol. 16(1), pages 1-15, December.
Handle:
RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-60801-6
DOI: 10.1038/s41467-025-60801-6
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-60801-6. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.