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Model interpretability

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This article describes methods you can use for model interpretability in Azure Machine Learning.

Important

With the release of the Responsible AI dashboard, which includes model interpretability, we recommend that you migrate to the new experience, because the older SDK v1 preview model interpretability dashboard will no longer be actively maintained.

Why model interpretability is important to model debugging

When you're using machine learning models in ways that affect people's lives, it's critically important to understand what influences the behavior of models. Interpretability helps answer questions in scenarios such as:

  • Model debugging: Why did my model make this mistake? How can I improve my model?
  • Human-AI collaboration: How can I understand and trust the model's decisions?
  • Regulatory compliance: Does my model satisfy legal requirements?

The interpretability component of the Responsible AI dashboard contributes to the "diagnose" stage of the model lifecycle workflow by generating human-understandable descriptions of the predictions of a machine learning model. It provides multiple views into a model's behavior:

  • Global explanations: For example, what features affect the overall behavior of a loan allocation model?
  • Local explanations: For example, why was a customer's loan application approved or rejected?

You can also observe model explanations for a selected cohort as a subgroup of data points. This approach is valuable when, for example, you're assessing fairness in model predictions for individuals in a particular demographic group. The Local explanation tab of this component also represents a full data visualization, which is great for general eyeballing of the data and looking at differences between correct and incorrect predictions of each cohort.

The capabilities of this component are founded by the InterpretML package, which generates model explanations.

Use interpretability when you need to:

  • Determine how trustworthy your AI system's predictions are by understanding what features are most important for the predictions.
  • Approach the debugging of your model by understanding it first and identifying whether the model is using healthy features or merely false correlations.
  • Uncover potential sources of unfairness by understanding whether the model is basing predictions on sensitive features or on features that are highly correlated with them.
  • Build user trust in your model's decisions by generating local explanations to illustrate their outcomes.
  • Complete a regulatory audit of an AI system to validate models and monitor the impact of model decisions on humans.

How to interpret your model

In machine learning, features are the data fields you use to predict a target data point. For example, to predict credit risk, you might use data fields for age, account size, and account age. Here, age, account size, and account age are features. Feature importance tells you how each data field affects the model's predictions. For example, although you might use age heavily in the prediction, account size and account age might not affect the prediction values significantly. Through this process, data scientists can explain resulting predictions in ways that give stakeholders visibility into the model's most important features.

By using the classes and methods in the Responsible AI dashboard and by using SDK v2 and CLI v2, you can:

  • Explain model prediction by generating feature-importance values for the entire model (global explanation) or individual data points (local explanation).
  • Achieve model interpretability on real-world datasets at scale.
  • Use an interactive visualization dashboard to discover patterns in your data and its explanations at training time.

By using the classes and methods in the SDK v1, you can:

  • Explain model prediction by generating feature-importance values for the entire model or individual data points.
  • Achieve model interpretability on real-world datasets at scale during training and inference.
  • Use an interactive visualization dashboard to discover patterns in your data and its explanations at training time.

Note

Model interpretability classes are made available through the SDK v1 package. For more information, see Install SDK packages for Azure Machine Learning and azureml.interpret.

Supported model interpretability techniques

The Responsible AI dashboard and azureml-interpret use the interpretability techniques that were developed in Interpret-Community, an open-source Python package for training interpretable models and helping to explain opaque-box AI systems. Opaque-box models are those for which we have no information about their internal workings.

Interpret-Community serves as the host for the following supported explainers, and currently supports the interpretability techniques presented in the next sections.

Supported in Responsible AI dashboard in Python SDK v2 and CLI v2

Interpretability technique Description Type
Mimic Explainer (Global Surrogate) + SHAP tree Mimic Explainer is based on the idea of training global surrogate models to mimic opaque-box models. A global surrogate model is an intrinsically interpretable model that's trained to approximate the predictions of any opaque-box model as accurately as possible.

Data scientists can interpret the surrogate model to draw conclusions about the opaque-box model. The Responsible AI dashboard uses LightGBM (LGBMExplainableModel), paired with the SHAP (SHapley Additive exPlanations) Tree Explainer, which is a specific explainer to trees and ensembles of trees. The combination of LightGBM and SHAP tree provides model-agnostic global and local explanations of your machine learning models.		 Model-agnostic

Supported model interpretability techniques for text models

Interpretability technique Description Type Text Task
SHAP text SHAP (SHapley Additive exPlanations) is a popular explanation method for deep neural networks that provides insights into the contribution of each input feature to a given prediction. It's based on the concept of Shapley values, which is a method for assigning credit to individual players in a cooperative game. SHAP applies this concept to the input features of a neural network by computing the average contribution of each feature to the model's output across all possible combinations of features. For text specifically, SHAP splits on words in a hierarchical manner, treating each word or token as a feature. This produces a set of attribution values that quantify the importance of each word or token for the given prediction. The final attribution map is generated by visualizing these values as a heatmap over the original text document. SHAP is a model-agnostic method and can be used to explain a wide range of deep learning models, including CNNs, RNNs, and transformers. Additionally, it provides several desirable properties, such as consistency, accuracy, and fairness, making it a reliable and interpretable technique for understanding the decision-making process of a model. Model Agnostic Text Multi-class Classification, Text Multi-label Classification

Supported model interpretability techniques for image models

Interpretability technique Description Type Vision Task
SHAP vision SHAP (SHapley Additive exPlanations) is a popular explanation method for deep neural networks that provides insights into the contribution of each input feature to a given prediction. It's based on the concept of Shapley values, which is a method for assigning credit to individual players in a cooperative game. SHAP applies this concept to the input features of a neural network by computing the average contribution of each feature to the model's output across all possible combinations of features. For vision specifically, SHAP splits on the image in a hierarchical manner, treating superpixel areas of the image as each feature. This produces a set of attribution values that quantify the importance of each superpixel or image area for the given prediction. The final attribution map is generated by visualizing these values as a heatmap. SHAP is a model-agnostic method and can be used to explain a wide range of deep learning models, including CNNs, RNNs, and transformers. Additionally, it provides several desirable properties, such as consistency, accuracy, and fairness, making it a reliable and interpretable technique for understanding the decision-making process of a model. Model Agnostic Image Multi-class Classification, Image Multi-label Classification
Guided Backprop Guided-backprop is a popular explanation method for deep neural networks that provides insights into the learned representations of the model. It generates a visualization of the input features that activate a particular neuron in the model, by computing the gradient of the output with respect to the input image. Unlike other gradient-based methods, guided-backprop only backpropagates through positive gradients and uses a modified ReLU activation function to ensure that negative gradients don't influence the visualization. This results in a more interpretable and high-resolution saliency map that highlights the most important features in the input image for a given prediction. Guided-backprop can be used to explain a wide range of deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. AutoML Image Multi-class Classification, Image Multi-label Classification
Guided gradCAM Guided GradCAM is a popular explanation method for deep neural networks that provides insights into the learned representations of the model. It generates a visualization of the input features that contribute most to a particular output class, by combining the gradient-based approach of guided backpropagation with the localization approach of GradCAM. Specifically, it computes the gradients of the output class with respect to the feature maps of the last convolutional layer in the network, and then weights each feature map according to the importance of its activation for that class. This produces a high-resolution heatmap that highlights the most discriminative regions of the input image for the given output class. Guided GradCAM can be used to explain a wide range of deep learning models, including CNNs, RNNs, and transformers. Additionally, by incorporating guided backpropagation, it ensures that the visualization is meaningful and interpretable, avoiding spurious activations and negative contributions. AutoML Image Multi-class Classification, Image Multi-label Classification
Integrated Gradients Integrated Gradients is a popular explanation method for deep neural networks that provides insights into the contribution of each input feature to a given prediction. It computes the integral of the gradient of the output class with respect to the input image, along a straight path between a baseline image and the actual input image. This path is typically chosen to be a linear interpolation between the two images, with the baseline being a neutral image that has no salient features. By integrating the gradient along this path, Integrated Gradients provides a measure of how each input feature contributes to the prediction, allowing for an attribution map to be generated. This map highlights the most influential input features, and can be used to gain insights into the model's decision-making process. Integrated Gradients can be used to explain a wide range of deep learning models, including CNNs, RNNs, and transformers. Additionally, it's a theoretically grounded technique that satisfies a set of desirable properties, such as sensitivity, implementation invariance, and completeness. AutoML Image Multi-class Classification, Image Multi-label Classification
XRAI XRAI is a novel region-based saliency method based on Integrated Gradients (IG). It over-segments the image and iteratively tests the importance of each region, coalescing smaller regions into larger segments based on attribution scores. This strategy yields high quality, tightly bounded saliency regions that outperform existing saliency techniques. XRAI can be used with any DNN-based model as long as there's a way to cluster the input features into segments through some similarity metric. AutoML Image Multi-class Classification, Image Multi-label Classification
D-RISE D-RISE is a model agnostic method for creating visual explanations for the predictions of object detection models. By accounting for both the localization and categorization aspects of object detection, D-RISE can produce saliency maps that highlight parts of an image that most contribute to the prediction of the detector. Unlike gradient-based methods, D-RISE is more general and doesn't need access to the inner workings of the object detector; it only requires access to the inputs and outputs of the model. The method can be applied to one-stage detectors (for example, YOLOv3), two-stage detectors (for example, Faster-RCNN), and Vision Transformers (for example, DETR, OWL-ViT).
D-Rise provides the saliency map by creating random masks of the input image and will send it to the object detector with the random masks of the input image. By assessing the change of the object detector's score, it aggregates all the detections with each mask and produce a final saliency map.		 Model Agnostic Object Detection

Supported in Python SDK v1

Interpretability technique Description Type
SHAP Tree Explainer The SHAP Tree Explainer, which focuses on a polynomial, time-fast, SHAP value-estimation algorithm that's specific to trees and ensembles of trees. Model-specific
SHAP Deep Explainer Based on the explanation from SHAP, Deep Explainer is a "high-speed approximation algorithm for SHAP values in deep learning models that builds on a connection with DeepLIFT described in the SHAP NIPS paper. TensorFlow models and Keras models using the TensorFlow back end are supported (there's also preliminary support for PyTorch)." Model-specific
SHAP Linear Explainer The SHAP Linear Explainer computes SHAP values for a linear model, optionally accounting for inter-feature correlations. Model-specific
SHAP Kernel Explainer The SHAP Kernel Explainer uses a specially weighted local linear regression to estimate SHAP values for any model. Model-agnostic
Mimic Explainer (Global Surrogate) Mimic Explainer is based on the idea of training global surrogate models to mimic opaque-box models. A global surrogate model is an intrinsically interpretable model that's trained to approximate the predictions of any opaque-box model as accurately as possible. Data scientists can interpret the surrogate model to draw conclusions about the opaque-box model. You can use one of the following interpretable models as your surrogate model: LightGBM (LGBMExplainableModel), Linear Regression (LinearExplainableModel), Stochastic Gradient Descent explainable model (SGDExplainableModel), or Decision Tree (DecisionTreeExplainableModel). Model-agnostic
Permutation Feature Importance Explainer Permutation Feature Importance (PFI) is a technique used to explain classification and regression models that's inspired by Breiman's Random Forests paper (see section 10). At a high level, the way it works is by randomly shuffling data one feature at a time for the entire dataset and calculating how much the performance metric of interest changes. The larger the change, the more important that feature is. PFI can explain the overall behavior of any underlying model but doesn't explain individual predictions. Model-agnostic

Besides the interpretability techniques described in the previous section, we support another SHAP-based explainer, called Tabular Explainer. Depending on the model, Tabular Explainer uses one of the supported SHAP explainers:

  • Tree Explainer for all tree-based models
  • Deep Explainer for deep neural network (DNN) models
  • Linear Explainer for linear models
  • Kernel Explainer for all other models

Tabular Explainer has also made significant feature and performance enhancements over the direct SHAP explainers:

  • Summarization of the initialization dataset: When speed of explanation is most important, we summarize the initialization dataset and generate a small set of representative samples. This approach speeds up the generation of overall and individual feature importance values.
  • Sampling the evaluation data set: If you pass in a large set of evaluation samples but don't actually need all of them to be evaluated, you can set the sampling parameter to true to speed up the calculation of overall model explanations.

The following diagram shows the current structure of supported explainers:

Diagram of Machine Learning Interpretability architecture.

Supported machine learning models

The azureml.interpret package of the SDK supports models that are trained with the following dataset formats:

  • numpy.array
  • pandas.DataFrame
  • iml.datatypes.DenseData
  • scipy.sparse.csr_matrix

The explanation functions accept both models and pipelines as input. If a model is provided, it must implement the prediction function predict or predict_proba that conforms to the Scikit convention. If your model doesn't support this, you can wrap it in a function that generates the same outcome as predict or predict_proba in Scikit and use that wrapper function with the selected explainer.

If you provide a pipeline, the explanation function assumes that the running pipeline script returns a prediction. When you use this wrapping technique, azureml.interpret can support models that are trained via PyTorch, TensorFlow, Keras deep learning frameworks, and classic machine learning models.

Local and remote compute target

The azureml.interpret package is designed to work with both local and remote compute targets. If you run the package locally, the SDK functions won't contact any Azure services.

You can run the explanation remotely on Azure Machine Learning Compute and log the explanation info into the Azure Machine Learning Run History Service. After this information is logged, reports and visualizations from the explanation are readily available on Azure Machine Learning studio for analysis.

Next steps