Building Responsible AI with Azure ML's RAI Dashboard

The Responsible AI Dashboard in Azure Machine Learning provides comprehensive tools for understanding, debugging, and improving your machine learning models. It brings together multiple responsible AI components in a unified interface.

Understanding the RAI Dashboard

The dashboard includes:

• Error Analysis
• Model Explanations
• Fairness Assessment
• Counterfactual Analysis
• Causal Inference

Setting Up RAI Dashboard

from azure.ai.ml import MLClient
from azure.ai.ml.entities import Model
from azure.identity import DefaultAzureCredential
from raiwidgets import ResponsibleAIDashboard
from responsibleai import RAIInsights

# Load your trained model and data
import joblib
import pandas as pd

model = joblib.load("model.pkl")
train_data = pd.read_csv("train.csv")
test_data = pd.read_csv("test.csv")

# Define target and features
target_column = "churn"
features = [col for col in train_data.columns if col != target_column]

# Create RAI Insights
rai_insights = RAIInsights(
    model=model,
    train=train_data,
    test=test_data,
    target_column=target_column,
    task_type="classification"
)

Adding Error Analysis

# Add error analysis component
rai_insights.error_analysis.add()

# Compute insights
rai_insights.compute()

# View error tree
error_tree = rai_insights.error_analysis.get_data()
print("Error tree nodes:", len(error_tree))

Model Explanations

# Add explainer
rai_insights.explainer.add()

# Compute explanations
rai_insights.compute()

# Get global feature importance
global_importance = rai_insights.explainer.get_global_importance()
for feature, importance in sorted(global_importance.items(), key=lambda x: -x[1])[:10]:
    print(f"{feature}: {importance:.4f}")

Fairness Assessment

# Add fairness analysis
rai_insights.fairness.add(
    sensitive_features=["gender", "age_group"]
)

# Compute fairness metrics
rai_insights.compute()

# Get fairness metrics
fairness_metrics = rai_insights.fairness.get_data()

# Check for disparities
for sensitive_feature in ["gender", "age_group"]:
    print(f"\nFairness metrics for {sensitive_feature}:")
    for metric_name, values in fairness_metrics[sensitive_feature].items():
        print(f"  {metric_name}: {values}")

Creating RAI Dashboard in Azure ML

from azure.ai.ml import MLClient
from azure.ai.ml.entities import (
    ManagedOnlineEndpoint,
    ManagedOnlineDeployment
)

# Register model with RAI components
from azure.ai.ml.entities import Model

model_for_rai = Model(
    path="./model",
    name="churn-model-rai",
    description="Model with RAI dashboard"
)

ml_client.models.create_or_update(model_for_rai)

# Create RAI insights job
from azure.ai.ml import dsl, Input, Output

@dsl.pipeline(
    compute="cpu-cluster",
    description="RAI Dashboard Pipeline"
)
def rai_pipeline(
    target_column_name: str,
    train_data: Input,
    test_data: Input,
    model: Input
):
    from azure.ai.ml.rai import (
        RAIInsightsPipelineComponent,
        ErrorAnalysisComponent,
        ExplanationComponent,
        FairnessComponent,
        CounterfactualComponent,
        CausalComponent
    )

    # Create RAI insights
    rai_constructor = RAIInsightsPipelineComponent(
        task_type="classification",
        model=model,
        train_dataset=train_data,
        test_dataset=test_data,
        target_column_name=target_column_name
    )

    # Add components
    error_analysis = ErrorAnalysisComponent(
        rai_insights=rai_constructor.outputs.rai_insights
    )

    explanations = ExplanationComponent(
        rai_insights=rai_constructor.outputs.rai_insights
    )

    fairness = FairnessComponent(
        rai_insights=rai_constructor.outputs.rai_insights,
        sensitive_features=["gender", "age"]
    )

    return {
        "dashboard": rai_constructor.outputs.rai_insights
    }

# Submit pipeline
pipeline_job = ml_client.jobs.create_or_update(
    rai_pipeline(
        target_column_name="churn",
        train_data=Input(path="azureml:train-data:1"),
        test_data=Input(path="azureml:test-data:1"),
        model=Input(path="azureml:churn-model:1")
    )
)

Interpreting the Dashboard

# Launch interactive dashboard
ResponsibleAIDashboard(rai_insights)

# Or save for sharing
rai_insights.save("./rai_insights_output")

# Load saved insights
loaded_insights = RAIInsights.load("./rai_insights_output")

Best Practices for Responsible AI

class ResponsibleMLPipeline:
    def __init__(self, model, train_data, test_data, target_column):
        self.model = model
        self.train_data = train_data
        self.test_data = test_data
        self.target_column = target_column
        self.rai_insights = None

    def run_full_assessment(self, sensitive_features=None):
        """Run complete RAI assessment"""
        self.rai_insights = RAIInsights(
            model=self.model,
            train=self.train_data,
            test=self.test_data,
            target_column=self.target_column,
            task_type="classification"
        )

        # Add all components
        self.rai_insights.error_analysis.add()
        self.rai_insights.explainer.add()

        if sensitive_features:
            self.rai_insights.fairness.add(sensitive_features=sensitive_features)

        self.rai_insights.counterfactual.add(
            total_CFs=10,
            desired_class="opposite"
        )

        # Compute all insights
        self.rai_insights.compute()

        return self.generate_report()

    def generate_report(self):
        """Generate RAI report"""
        report = {
            "model_performance": self._get_performance_metrics(),
            "error_analysis": self._get_error_summary(),
            "feature_importance": self._get_top_features(),
            "fairness": self._get_fairness_summary(),
            "recommendations": self._get_recommendations()
        }
        return report

    def _get_recommendations(self):
        """Generate actionable recommendations"""
        recommendations = []

        # Check for high error cohorts
        error_data = self.rai_insights.error_analysis.get_data()
        # Add recommendations based on findings

        return recommendations

The RAI Dashboard enables you to build and deploy ML models that are fair, interpretable, and reliable.