Mitigating data bias and ensuring reliable evaluation of AI models with shortcut hull learning
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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60801-6 |
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| _version_ | 1849238260959674368 |
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| author | Wenhao Zhou Faqiang Liu Hao Zheng Rong Zhao |
| author_facet | Wenhao Zhou Faqiang Liu Hao Zheng Rong Zhao |
| author_sort | Wenhao Zhou |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e38fb26ac75640f29a6361301313c3a9 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-e38fb26ac75640f29a6361301313c3a92025-08-20T04:01:41ZengNature PortfolioNature Communications2041-17232025-07-0116111510.1038/s41467-025-60801-6Mitigating data bias and ensuring reliable evaluation of AI models with shortcut hull learningWenhao Zhou0Faqiang Liu1Hao Zheng2Rong Zhao3Center for Brain-Inspired Computing Research (CBICR), Tsinghua UniversityCenter for Brain-Inspired Computing Research (CBICR), Tsinghua UniversityCenter for Brain-Inspired Computing Research (CBICR), Tsinghua UniversityCenter for Brain-Inspired Computing Research (CBICR), Tsinghua UniversityAbstract 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.https://doi.org/10.1038/s41467-025-60801-6 |
| spellingShingle | Wenhao Zhou Faqiang Liu Hao Zheng Rong Zhao Mitigating data bias and ensuring reliable evaluation of AI models with shortcut hull learning Nature Communications |
| title | Mitigating data bias and ensuring reliable evaluation of AI models with shortcut hull learning |
| title_full | Mitigating data bias and ensuring reliable evaluation of AI models with shortcut hull learning |
| title_fullStr | Mitigating data bias and ensuring reliable evaluation of AI models with shortcut hull learning |
| title_full_unstemmed | Mitigating data bias and ensuring reliable evaluation of AI models with shortcut hull learning |
| title_short | Mitigating data bias and ensuring reliable evaluation of AI models with shortcut hull learning |
| title_sort | mitigating data bias and ensuring reliable evaluation of ai models with shortcut hull learning |
| url | https://doi.org/10.1038/s41467-025-60801-6 |
| work_keys_str_mv | AT wenhaozhou mitigatingdatabiasandensuringreliableevaluationofaimodelswithshortcuthulllearning AT faqiangliu mitigatingdatabiasandensuringreliableevaluationofaimodelswithshortcuthulllearning AT haozheng mitigatingdatabiasandensuringreliableevaluationofaimodelswithshortcuthulllearning AT rongzhao mitigatingdatabiasandensuringreliableevaluationofaimodelswithshortcuthulllearning |