Towards a Unified Evaluation of Explanation Methods without Ground Truth
This paper proposes a set of criteria to evaluate the objectiveness of explanation methods of neural networks, which is crucial for the development of explainable AI, but it also presents significant challenges. The core challenge is that people usually cannot obtain ground-truth explanations of the neural network. To this end, we design four metrics to evaluate the explanation result without ground-truth explanations. Our metrics can be broadly applied to nine benchmark methods of interpreting neural networks, which provides new insights of explanation methods.
The paper proposes four metrics to evaluate explanation methods, namely 1) bias of the attribution map based on Shapley value, 2) unexplainable feature components, 3) robustness of explanations, and 4) mutual verification (whether two explanation methods can verify each other). These metrics focus on explanation methods that give attribution maps as outputs (such as a pixel-level attention or saliency map). Besides, it also evaluates nine widely used explanation approaches with these metrics. I would recommend for acceptance. Several explanation methods have been proposed to generate attribution maps. However, it is shown that it is hard to inspect the quality of these explanation methods visually (e.g., as mentioned in http://arxiv.org/abs/1810.03292). I believe the proposed metrics provide valuable insights on quantifying the performance of explanation methods. The evaluation results on nine existing approaches are also helpful to future work on explainable AI. However, I would also like to note that while the four metrics proposed in the paper reflect the desired properties of explanation methods, they should not be the only criteria. The desired criteria depend on the application, and methods without these properties could also be potentially useful or even better in some cases. For example, instead of giving an unbiased attribution map, it would be sometimes more helpful to provide a discrete binary mask of regions of interest (e.g., for downstream image processing).
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Towards a Unified Evaluation of Explanation Methods without Ground Truth
This paper proposes a set of criteria to evaluate the objectiveness of explanation methods of neural networks, which is crucial for the development of explainable AI, but it also presents significant challenges. The core challenge is that people usually cannot obtain ground-truth explanations of the neural network. To this end, we design four metrics to evaluate the explanation result without ground-truth explanations. Our metrics can be broadly applied to nine benchmark methods of interpreting neural networks, which provides new insights of explanation methods.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The paper proposes four metrics to evaluate explanation methods, namely 1) bias of the attribution map based on Shapley value, 2) unexplainable feature components, 3) robustness of explanations, and 4) mutual verification (whether two explanation methods can verify each other). These metrics focus on explanation methods that give attribution maps as outputs (such as a pixel-level attention or saliency map). Besides, it also evaluates nine widely used explanation approaches with these metrics. I would recommend for acceptance. Several explanation methods have been proposed to generate attribution maps. However, it is shown that it is hard to inspect the quality of these explanation methods visually (e.g., as mentioned in http://arxiv.org/abs/1810.03292). I believe the proposed metrics provide valuable insights on quantifying the performance of explanation methods. The evaluation results on nine existing approaches are also helpful to future work on explainable AI. However, I would also like to note that while the four metrics proposed in the paper reflect the desired properties of explanation methods, they should not be the only criteria. The desired criteria depend on the application, and methods without these properties could also be potentially useful or even better in some cases. For example, instead of giving an unbiased attribution map, it would be sometimes more helpful to provide a discrete binary mask of regions of interest (e.g., for downstream image processing).
assistant
The paper proposes four metrics to evaluate explanation methods, namely 1) bias of the attribution map based on Shapley value, 2) unexplainable feature components, 3) robustness of explanations, and 4) mutual verification (whether two explanation methods can verify each other). These metrics focus on explanation methods that give attribution maps as outputs (such as a pixel-level attention or saliency map). Besides, it also evaluates nine widely used explanation approaches with these metrics. I would recommend for acceptance. Several explanation methods have been proposed to generate attribution maps. However, it is shown that it is hard to inspect the quality of these explanation methods visually (e.g., as mentioned in http://arxiv.org/abs/1810.03292). I believe the proposed metrics provide valuable insights on quantifying the performance of explanation methods. The evaluation results on nine existing approaches are also helpful to future work on explainable AI. However, I would also like to note that while the four metrics proposed in the paper reflect the desired properties of explanation methods, they should not be the only criteria. The desired criteria depend on the application, and methods without these properties could also be potentially useful or even better in some cases. For example, instead of giving an unbiased attribution map, it would be sometimes more helpful to provide a discrete binary mask of regions of interest (e.g., for downstream image processing).