arXiv:2606.04009v1 Announce Type: cross Abstract: Two-sample testing is a fundamental tool for detecting distributional differences across scientific domains, but classical tests (including kernel-based tests) can be ineffective on high-dimensional structured data such as images. Recent deep two-sample tests improve sensitivity in these settings by learning informative representations, yet they provide limited insight into which data features drive rejection of the null hypothesis $H_0$. To address this issue, we propose a counterfactual explanation framework for deep two-sample testing that g
The increasing complexity of deep learning models necessitates better interpretability, particularly in critical applications where understanding 'why' is as important as 'what'.
This development enhances the explainability of advanced AI models, fostering greater trust and enabling more effective debugging and validation in high-stakes domains.
Deep learning models in two-sample testing can now offer counterfactual explanations, moving beyond mere detection of differences to identifying the specific features driving those differences.
- · AI researchers
- · Data scientists
- · Regulatory bodies
- · Industries requiring explainable AI
- · Black-box AI proponents
- · Systems relying solely on statistical significance
Increased adoption of deep two-sample testing in sensitive areas like medical diagnostics or fraud detection due to improved transparency.
Development of new AI models inherently designed for explainability, shifting the focus from pure performance to interpretable performance.
Potential for new ethical guidelines and regulatory frameworks specifically addressing the interpretability and accountability of complex AI systems.
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Read at arXiv cs.LG