Mitigating data bias and ensuring reliable evaluation of AI models with shortcut hull learning

Area of Science:

• Artificial Intelligence
• Machine Learning
• Computer Vision

Background:

• Shortcut learning, driven by dataset biases, challenges AI interpretability and robustness.
• Identifying and mitigating these unintended correlations in high-dimensional data is complex.

Purpose of the Study:

• Introduce shortcut hull learning, a novel diagnostic paradigm for identifying AI shortcuts.
• Establish a comprehensive, shortcut-free evaluation framework for AI models.
• Empirically investigate deep neural networks' learning capacity beyond representational analysis.

Main Methods:

• Unifying shortcut representations in probability space.
• Utilizing diverse models with varied inductive biases to detect shortcuts.
• Developing a shortcut-free topological dataset for rigorous evaluation.

Main Results:

• The proposed framework enables efficient learning and identification of shortcuts.
• Convolutional models unexpectedly outperformed transformer-based models in global capability assessments.
• The study challenges prevailing assumptions about model architectures and their capabilities.

Conclusions:

• Shortcut hull learning provides a robust and bias-free evaluation method.
• The framework uncovers true model capabilities, independent of architectural biases.
• This research advances AI interpretability and reliability by addressing shortcut learning.