arXiv:2606.10877v1 Announce Type: new Abstract: Occlusion-based attribution methods provide an intuitive way to estimate feature importance by perturbing input features and measuring the resulting change in model output. However, their reliability is strongly affected by how feature removal is implemented: externally selected baselines can introduce bias, out-of-distribution samples, and unstable explanations, while in nonlinear models the occlusion of a set of features can also alter the contribution of non-occluded features. We refer to this effect as attribution shift, as the attribution sc
The increasing complexity and opacity of AI models necessitate more robust and reliable explainability methods, pushing research towards dynamic attribution techniques like training-guided occlusion.
Improved feature attribution is critical for developing trustworthy and verifiable AI systems, particularly in sensitive applications where understanding model decisions is paramount.
Current static occlusion methods are shown to be limited, advocating for dynamic, training-guided approaches that account for the interdependencies of features and model nonlinearities.
- · AI Safety Researchers
- · Developers of robust AI models
- · Industries requiring verifiable AI (e.g., healthcare, finance)
- · AI explanation methods relying on naive occlusion
- · Users of black-box AI where interpretability is crucial
This research will lead to more accurate and stable feature importance estimations for complex AI models.
Enhanced interpretability will foster greater public and regulatory trust in advanced AI applications, potentially accelerating their adoption in critical sectors.
The ability to accurately attribute features could lead to more efficient debugging and adversarial robustness strategies for AI, influencing model design paradigms.
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