A Dual-Perspective Approach to Evaluating Feature Attribution Methods
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929602673246208 |
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| author | Li, Yawei Zhang, Yang Kawaguchi, Kenji Khakzar, Ashkan Bischl, Bernd Rezaei, Mina |
| author_facet | Li, Yawei Zhang, Yang Kawaguchi, Kenji Khakzar, Ashkan Bischl, Bernd Rezaei, Mina |
| contents | Feature attribution methods attempt to explain neural network predictions by identifying relevant features. However, establishing a cohesive framework for assessing feature attribution remains a challenge. There are several views through which we can evaluate attributions. One principal lens is to observe the effect of perturbing attributed features on the model's behavior (i.e., faithfulness). While providing useful insights, existing faithfulness evaluations suffer from shortcomings that we reveal in this paper. In this work, we propose two new perspectives within the faithfulness paradigm that reveal intuitive properties: soundness and completeness. Soundness assesses the degree to which attributed features are truly predictive features, while completeness examines how well the resulting attribution reveals all the predictive features. The two perspectives are based on a firm mathematical foundation and provide quantitative metrics that are computable through efficient algorithms. We apply these metrics to mainstream attribution methods, offering a novel lens through which to analyze and compare feature attribution methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2308_08949 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Dual-Perspective Approach to Evaluating Feature Attribution Methods Li, Yawei Zhang, Yang Kawaguchi, Kenji Khakzar, Ashkan Bischl, Bernd Rezaei, Mina Machine Learning Artificial Intelligence Feature attribution methods attempt to explain neural network predictions by identifying relevant features. However, establishing a cohesive framework for assessing feature attribution remains a challenge. There are several views through which we can evaluate attributions. One principal lens is to observe the effect of perturbing attributed features on the model's behavior (i.e., faithfulness). While providing useful insights, existing faithfulness evaluations suffer from shortcomings that we reveal in this paper. In this work, we propose two new perspectives within the faithfulness paradigm that reveal intuitive properties: soundness and completeness. Soundness assesses the degree to which attributed features are truly predictive features, while completeness examines how well the resulting attribution reveals all the predictive features. The two perspectives are based on a firm mathematical foundation and provide quantitative metrics that are computable through efficient algorithms. We apply these metrics to mainstream attribution methods, offering a novel lens through which to analyze and compare feature attribution methods. |
| title | A Dual-Perspective Approach to Evaluating Feature Attribution Methods |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2308.08949 |