Measuring Explainer Stability via Attribution Separability
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arXiv:2608.02697v1 Announce Type: new Abstract: Attribution methods (AMs) assign an importance score to each feature and are widely adopted to explain black-box models. However, most methods can produce variable attribution scores due to stochastic components in their definition. In this paper, we propose a distribution-based framework to capture the stability of attribution scores. In particular, our approach allows to understand the degree of separability in the ranked attribution vector and…
1Key Takeaways
- arXiv:2608.02697v1 Announce Type: new Abstract: Attribution methods (AMs) assign an importance score to each feature and are widely adopted to explain black-box models.
- However, most methods can produce variable attribution scores due to stochastic components in their definition.
- In this paper, we propose a distribution-based framework to capture the stability of attribution scores.
- In particular, our approach allows to understand the degree of separability in the ranked attribution vector and….
2AIWedia Score
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3Why it matters
Research breakthroughs often arrive in products months later—early signals matter for strategy. arXiv ML reports that arXiv:2608.02697v1 Announce Type: new Abstract: Attribution methods (AMs) assign an importance score to each feature and are widely adopted to explain black-box models.
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