Explainable AI

Feature Attributions

Feature attributions depict how much each feature in your model contributed to each instance of test data’s predictions. When you run explainable AI techniques, you get the predictions and feature attribution information.

Understanding the Feature Attribution Methods

LIME

LIME is termed “Local Interpretable Model Agnostic Explanation.” It explains any model by approximating it locally with an interpretable model. It first creates a sample dataset locally by permuting the features or values from the original test instance. Then, a linear model is fitted to the perturbed dataset to understand the contribution of each feature. The linear model gives the final weight of each feature after fitting, which is the LIME value of these features. LIME has several methods based on the different models’ architecture and the input data.

Figure 1: (a) LIME explanation for logistic regression model trained on iris dataset and
(b) LIME explanation for linear regression trained on Boston housing dataset.
Figure 2: LIME explanation for BERT model trained on BBC news dataset.
Figure 3: LIME explanation for image classification model on cats and dog dataset.

GradCAM

GradCAM stands for Gradient-weighted Class Activation Mapping. It uses the gradients of any target concept (say, “dog” in a classification network) flowing into the final convolutional layer. It works by evaluating the predicted class’s score gradients concerning the convolutional layer’s feature maps, which are then pooled to determine the weighted combination of feature maps. These weights are passed through the ReLu activation function to get the positive activations, producing a coarse localization map highlighting the critical regions in the image for predicting the concept.

Figure 4: GradCAM result for VGG16 model trained on human activity recognition dataset.

Deep Taylor

It is a method to decompose the output of the network classification into the contributions (relevance) of its input elements using Taylor’s theorem, which gives an approximation of a differentiable function. The output neuron is first decomposed into input neurons in a neural network. Then, the decomposition of these neurons is redistributed to their inputs, and the redistribution process is repeated until the input variables are reached. Thus, we get the relevance of input variables in the classification output.

Figure 5: Deep Taylor result for VGG16 model trained on human activity recognition dataset.

SHAP

Shapley values are a concept in a cooperative game theory algorithm that assigns credit to each player in a game for a particular outcome. When applied to machine learning models, this indicates that each model feature is considered as a “player” in the game, with AI Explanations allocating each proportionate feature credit for the prediction’s result. SHAP provides various methods based on model architecture to calculate Shapley values for different models.

Figure 6: SHAP result for VGG16 model trained on human activity recognition dataset.
Figure 7: SHAP results for YOLOv3 model trained on the COCO dataset.
Figure 8: (a) SHAP result for text classification using BERT
Figure 8: (b) SHAP result for text summarization using Schleifer/distilbart-cnn-12–6 model.

SODEx

The Surrogate Object Detection Explainer (SODEx) explains an object detection model using LIME, which explains a single prediction with a linear surrogate model. It first segments the image into super pixels and generates perturbed samples. Then, the black box model predicts the result of every perturbed observation. Using the dataset of perturbed samples and their responses, it trains a surrogate linear model that provides super pixel weights.

Figure 9: SODEx result for YOLOv3 model trained on the COCO dataset.

SegGradCAM

SEG-GRAD-CAM is an extension of Grad-CAM for semantic segmentation. It can generate heat maps to explain the relevance of the decisions of individual pixels or regions in the input image. The GradCAM uses the gradient of the logit for the predicted class with respect to chosen feature layers to determine their general relevance. But a CNN for semantic segmentation produces logits for every pixel and class. This idea allows us to adapt GradCAM to a semantic segmentation network flexibly since we can determine the gradient of the logit of just a single pixel, or pixels of an object instance, or simply all pixels of the image.

Figure 10: SegGradCAM result for U-net model trained on camvid dataset.

Conclusion

Explainable AI builds confidence in the model’s behavior by ensuring that the model does not focus on idiosyncratic details of the training data that will not generalize to unseen data. Therefore, it guarantees the fairness of ML models. We have implemented the explainable AI techniques for the models trained on tabular, text, and image data, thus enhancing these models’ transparency and interactivity. You can then use this information to verify that the model is behaving as expected, recognize bias in your models, and get ideas for improving your model and training data.

  1. Selvaraju et al. “Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization.” International Journal of Computer Vision 2019
  2. http://www.heatmapping.org/slides/2018_ICIP_2.pdf
  3. https://towardsdatascience.com/shap-shapley-additive-explanations-5a2a271ed9c3
  4. Sejr, J.H.; Schneider-Kamp, P.; Ayoub, N. Surrogate Object Detection Explainer (SODEx) with YOLOv4 and LIME
  5. https://www.steadforce.com/blog/explainable-object-detection
  6. Vinogradova, K., Dibrov, A., & Myers, G. (2020). Towards Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping

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