Distribution-Guided Local Explanation for Black-Box Classifiers
Existing local explanation methods provide an explanation for each decision of black-box classifiers, in the form of relevance scores of features according to their contributions. To obtain satisfying explainability, many methods introduce ad hoc constraints into the classification loss to regularize these relevance scores. However, the large information gap between the classification loss and these constraints increases the difficulty of tuning hyper-parameters. To bridge this gap, in this paper we present a simple but effective mask predictor. Specifically, we model the above constraints with a distribution controller, and integrate it with a neural network to directly guide the distribution of relevance scores. The benefit of this strategy is to facilitate the setting of involved hyper-parameters, and enable discriminative scores over supporting features. The experimental results demonstrate that our method outperforms others in terms of faithfulness and explainability. Meanwhile, it also provides effective saliency maps for explaining each decision.
This paper proposes a mask predictor to bridge the information gap between the classification loss and the constraints introduced to obtain satisfying explainability. The predictor consists of a distribution controller and a mask generator to refine the mask towards the desired score distribution. The predictor is optimized solely under the classification loss without additional constraints, which therefore improves the faithfulness of mimicking target black-box models. This paper introduces two metrics to evaluate the proposed mask predictor and the experiments demonstrate the proposed method outperforms others in terms of faithfulness and explainability. Overall, this paper has some advantages: (1) the method could be a significant contribution to improve the local explanation for black box. The mask predictor can transform the output of the mask generator to establish the right-skewed distributions for relevance scores with the designed controller, which can decrease the difficulty of tuning the hyper-parameters. To solve the problem of the ad hoc constraints, the distribution controller models these above constraints and then is integrated with a neural network to directly guide the distribution of relevance scores. (2) the paper develops two metrics in terms of faithfulness and explainability to evaluate the effectiveness of the proposed method quantitatively. And compared with other baseline methods, it also generates saliency maps to visually demonstrate the explainability. All the experimental results demonstrate that the performance of the proposed method is better than others. (3) the paper states the motivation and the proposed algorithm clearly. The code is given. However, for the experiments, the following should be addressed. (1) It would be better to evaluate the proposed method with other networks as black boxes. (2) More detailed analysis should be added on the experimental results. (3) It should compare the explainability with some of the latest baseline methods. (4) It would be better to conduct the experiments on more datasets. Moreover, there are some minor comments: (1) Please state more details about the innovation of the method. (2) Figure 1 needs to be polished, for example the font.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Distribution-Guided Local Explanation for Black-Box Classifiers
Existing local explanation methods provide an explanation for each decision of black-box classifiers, in the form of relevance scores of features according to their contributions. To obtain satisfying explainability, many methods introduce ad hoc constraints into the classification loss to regularize these relevance scores. However, the large information gap between the classification loss and these constraints increases the difficulty of tuning hyper-parameters. To bridge this gap, in this paper we present a simple but effective mask predictor. Specifically, we model the above constraints with a distribution controller, and integrate it with a neural network to directly guide the distribution of relevance scores. The benefit of this strategy is to facilitate the setting of involved hyper-parameters, and enable discriminative scores over supporting features. The experimental results demonstrate that our method outperforms others in terms of faithfulness and explainability. Meanwhile, it also provides effective saliency maps for explaining each decision.
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>



This paper proposes a mask predictor to bridge the information gap between the classification loss and the constraints introduced to obtain satisfying explainability. The predictor consists of a distribution controller and a mask generator to refine the mask towards the desired score distribution. The predictor is optimized solely under the classification loss without additional constraints, which therefore improves the faithfulness of mimicking target black-box models. This paper introduces two metrics to evaluate the proposed mask predictor and the experiments demonstrate the proposed method outperforms others in terms of faithfulness and explainability. Overall, this paper has some advantages: (1) the method could be a significant contribution to improve the local explanation for black box. The mask predictor can transform the output of the mask generator to establish the right-skewed distributions for relevance scores with the designed controller, which can decrease the difficulty of tuning the hyper-parameters. To solve the problem of the ad hoc constraints, the distribution controller models these above constraints and then is integrated with a neural network to directly guide the distribution of relevance scores. (2) the paper develops two metrics in terms of faithfulness and explainability to evaluate the effectiveness of the proposed method quantitatively. And compared with other baseline methods, it also generates saliency maps to visually demonstrate the explainability. All the experimental results demonstrate that the performance of the proposed method is better than others. (3) the paper states the motivation and the proposed algorithm clearly. The code is given. However, for the experiments, the following should be addressed. (1) It would be better to evaluate the proposed method with other networks as black boxes. (2) More detailed analysis should be added on the experimental results. (3) It should compare the explainability with some of the latest baseline methods. (4) It would be better to conduct the experiments on more datasets. Moreover, there are some minor comments: (1) Please state more details about the innovation of the method. (2) Figure 1 needs to be polished, for example the font.
assistant
This paper proposes a mask predictor to bridge the information gap between the classification loss and the constraints introduced to obtain satisfying explainability. The predictor consists of a distribution controller and a mask generator to refine the mask towards the desired score distribution. The predictor is optimized solely under the classification loss without additional constraints, which therefore improves the faithfulness of mimicking target black-box models. This paper introduces two metrics to evaluate the proposed mask predictor and the experiments demonstrate the proposed method outperforms others in terms of faithfulness and explainability. Overall, this paper has some advantages: (1) the method could be a significant contribution to improve the local explanation for black box. The mask predictor can transform the output of the mask generator to establish the right-skewed distributions for relevance scores with the designed controller, which can decrease the difficulty of tuning the hyper-parameters. To solve the problem of the ad hoc constraints, the distribution controller models these above constraints and then is integrated with a neural network to directly guide the distribution of relevance scores. (2) the paper develops two metrics in terms of faithfulness and explainability to evaluate the effectiveness of the proposed method quantitatively. And compared with other baseline methods, it also generates saliency maps to visually demonstrate the explainability. All the experimental results demonstrate that the performance of the proposed method is better than others. (3) the paper states the motivation and the proposed algorithm clearly. The code is given. However, for the experiments, the following should be addressed. (1) It would be better to evaluate the proposed method with other networks as black boxes. (2) More detailed analysis should be added on the experimental results. (3) It should compare the explainability with some of the latest baseline methods. (4) It would be better to conduct the experiments on more datasets. Moreover, there are some minor comments: (1) Please state more details about the innovation of the method. (2) Figure 1 needs to be polished, for example the font.