In the era of artificial intelligence and machine learning, understanding and trusting AI systems becomes crucial. This article delves into Explainable AI (XAI) and Azure Machine Learning, which aim to make AI models more interpretable and transparent.
Azure Machine Learning offers a suite of tools and capabilities for building, training, and deploying explainable ML models in the cloud, including local and global model explainers that provide valuable insights into ML models. With applications in various domains such as healthcare, finance, and transportation, Azure Machine Learning is emerging as a powerful platform for creating more transparent and trustworthy AI solutions.
What Is Explainable AI?
Explainable AI (XAI) is a concept in artificial intelligence that aims to create models and algorithms that can be understood by humans. Unlike traditional AI, which can be opaque and difficult to interpret, XAI aims to provide explanations and justifications for the decisions made by AI models. Explainability is important because it can help build trust in AI systems and increase their adoption in various fields.
XAI approaches include model-agnostic methods, such as feature importance and decision tree analysis, as well as model-specific methods, such as rule extraction and attention mechanisms. XAI techniques can be used to explain and interpret the predictions of machine learning (ML) models, the behavior of autonomous agents, and the outcomes of automated decision-making processes.
Explainable AI has applications in various domains, including healthcare, finance, and transportation. For example, XAI can help doctors understand the decisions made by medical AI systems, assist regulators in auditing financial models, and improve the safety and transparency of autonomous vehicles.
What Is Azure Machine Learning?
Azure Machine Learning (Azure ML) is a cloud-based service offered by Microsoft that enables developers and data scientists to build, manage, and deploy machine learning models at various scales. Azure ML provides a useful capabilities for machine learning workflows, including the following:
- Data preparation and cleaning: Azure ML offers data preparation and cleaning capabilities, including data transformation, missing value imputation, and outlier detection.
- Model training and tuning: Azure ML provides tools for training and tuning machine learning models, such as automated machine learning, hyperparameter tuning, and deep learning frameworks.
- Model evaluation and interpretation: Azure ML offers tools for evaluating and interpreting machine learning models, such as model explainability and fairness metrics.
- Deployment and management: Azure ML enables users to deploy machine learning models as web services or containers, and manage them using Azure’s DevOps tools.
- Integration with other Azure services: Azure ML integrates with other Azure services, such as Azure Databricks, Azure Synapse Analytics, and Azure Cognitive Services, for building end-to-end machine learning solutions.
Azure Machine Learning also offers the following tools that data scientists and developers can use for their ML projects:
- Azure ML Studio: a web-based tool for building and deploying machine learning models using a drag-and-drop interface.
- Azure ML Python SDK: a Python package for building machine learning workflows using Python code.
- Azure ML Designer: a visual interface for building and deploying machine learning pipelines using pre-built modules.
- Designer: Provides drag-and-drop ML modules for building experiments.
- The machine learning CLI: A command-line tool for managing ML workloads.
- Mimic Explainer: A feature that provides explanations for the decisions made by machine learning models, using techniques such as feature importance and SHAP values.
Azure Machine Learning provides several cost management features that can help businesses to optimize their usage and control their costs. By leveraging these cost management features, businesses can optimize their usage of Azure Machine Learning and control their costs, while still delivering high-quality machine learning solutions to their customers and stakeholders.
Explainable AI in the Azure Cloud
Azure Machine Learning includes features for explainable AI, including model explanations, which provide a detailed breakdown of the features and factors that the model is using to make its predictions. This can help users to understand how the model is making decisions, and to identify any biases or inaccuracies in the model’s predictions.
Another technique supported by Azure Machine Learning is the use of counterfactual explanations, which provide alternative scenarios that could have led to a different decision. This can be useful in situations where a user wants to understand how changing certain variables would affect the model’s predictions.
Azure Machine Learning also provides tools for evaluating the fairness and robustness of machine learning models, to ensure that they are not biased or vulnerable to attacks or errors.
Explaining Machine Learning Models Using Azure Machine Learning
Azure Machine Learning offers an SDK that can be used to build model explainers, even for models that were trained using non-Azure ML experiments. If you can use a script or estimator to train the model in Azure Machine Learning, you can build an explainer and use its explanations to perform analytics tasks.
To generate an explanation for your ML experiment’s script, you must ensure that the right packages (azureml-interpret and azureml-contrib-interpret) are installed in your execution environment. You can then use the packages to explain the trained model and upload the explanation to your output.
Step 1: Download the required packages
pip3 install azureml-interpret
pip3 install azureml-contrib-interpret
Step 2: Add code to an experiment script
For example, you could include this code in the experiment script to produce an explanation:
# Import the Azure ML execution library
from azureml.core.run import Run
from azureml.interpret import ExplanationClient
from interpret.ext.blackbox import TabularExplainer
from sklearn.datasets import load_breast_cancer
from sklearn import svm
from sklearn.model_selection import train_test_split
from azureml.core import Workspace
from azureml.core import Experiment
# Retrieve the run context for the experiment
ws = Workspace.from_config()
exp = Experiment(ws, ‘example’)
# Insert the code for training the model
breast_cancer_data = load_breast_cancer()
classes = breast_cancer_data.target_names.tolist()
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(breast_cancer_data.data,
breast_cancer_data.target,
test_size=0.2,
random_state=0)
clf = svm.SVC(gamma=0.001, C=100., probability=True)
model = clf.fit(x_train, y_train)
# Get the explanation
explainer = TabularExplainer(model,x_train)
explanation = explainer.explain_global(x_test)
# Get an explanation client to upload the model explanation
explain_client = ExplanationClient.from_run(run)
explain_client.upload_model_explanation(explanation, comment=’Tabular Explanation’)
run.complete()
The output should be similar to this:
Step 3: View model explanations
To view your model’s explanation, go to the Explanations tab for the specific run under Azure Machine Learning Studio.
Alternatively, you can download the explanation using the object ExplanationClient, which will produce a Python download:
Note: You can also get the run_id from Azure ML interface as shown below:
Azure Machine Learning Studio offers explanations with several visualizations to help measure feature importance. The first visualization for a given run (available from the Explanations tab) presents the global feature importance:
To view local feature importance values for individual features, you can switch to the visualization called Summary Importance:
Conclusion
In conclusion, explainable AI is increasingly important for machine learning and AI projects, particularly in domains where accountability and transparency are critical, such as finance and healthcare.
Azure Machine Learning provides a range of tools and capabilities for building, training, and deploying explainable ML models in the cloud. These tools include local and global model explainers, which can provide valuable insights into the inner workings of ML models and help to improve their transparency and trustworthiness.
Additionally, Azure ML provides a range of other features and capabilities, such as automated machine learning, deep learning frameworks, and scalable computing resources, which make it a powerful platform for building and deploying machine learning solutions in the cloud.
Author Bio: Gilad David Maayan
Gilad David Maayan is a technology writer who has worked with over 150 technology companies including SAP, Imperva, Samsung NEXT, NetApp and Check Point, producing technical and thought leadership content that elucidates technical solutions for developers and IT leadership. Today he heads Agile SEO, the leading marketing agency in the technology industry.
LinkedIn: https://www.linkedin.com/in/giladdavidmaayan/
