Explainable AI (XAI) Architecture Blueprint

Complete Architecture Overview

This Explainable AI (XAI) Architecture Blueprint outlines a scalable, secure, and observable cloud-native platform built on AWS for transparent AI decision-making. It integrates XAI models hosted on SageMaker with interpreters like SHAP and LIME for explainability, and includes React-based UI components for visualizing feature importance and predictions. CloudFront and API Gateway with WAF handle client access, while DynamoDB, S3, and ElastiCache manage data and model storage. Observability is ensured via CloudWatch and X-Ray, with security enforced by IAM, VPC, and KMS. The architecture supports transparent and interpretable AI workloads for enterprise applications.

Architecture Principles

Key Performance Metrics

1. Client Access Architecture

This diagram illustrates the client-side architecture, where Web Apps (React) and Mobile Apps (iOS/SwiftUI, Android/Kotlin) interact with the platform via CloudFront and API Gateway. CloudFront serves as a global CDN, caching static assets in S3 and executing edge logic with Lambda@Edge for request normalization. Web apps leverage CloudFront for low-latency delivery of visualizations, while mobile apps connect to API Gateway for dynamic XAI inference endpoints.

Key Features

// Sample React Component for XAI Visualization
import React from 'react';
import { LineChart } from 'recharts';

const FeatureImportanceChart = ({ data }) => (
  
    
    
    
    
);

export default FeatureImportanceChart;
                

2. API Gateway Architecture

This diagram details the API Gateway layer, the secure entry point for client requests from the Client Access Architecture (Diagram 1). It integrates with a Lambda Authorizer for JWT validation using Cognito User Pool and KMS for secure key management. The gateway routes requests to the XAI Compute Layer (Diagram 3) for model inference and explainability, protected by WAF rules to block malicious traffic.

Gateway Configuration

# Terraform for API Gateway
resource "aws_api_gateway_rest_api" "xai_platform" {
  name        = "xai-platform-api"
  description = "API Gateway for XAI Platform"
  endpoint_configuration {
    types = ["REGIONAL"]
  }
}

resource "aws_api_gateway_authorizer" "jwt" {
  name          = "jwt-authorizer"
  rest_api_id   = aws_api_gateway_rest_api.xai_platform.id
  authorizer_uri = aws_lambda_function.authorizer.invoke_arn
  type          = "TOKEN"
  identity_source = "method.request.header.Authorization"
}

resource "aws_wafv2_web_acl_association" "api" {
  resource_arn = aws_api_gateway_stage.prod.arn
  web_acl_arn  = aws_wafv2_web_acl.api.arn
}
                

3. XAI Compute Layer Architecture

This diagram focuses on the XAI Compute Layer, which hosts SageMaker for training and deploying XAI models (e.g., XGBoost, neural networks). Requests are routed from the API Gateway (Diagram 2) to SageMaker endpoints. This layer integrates with the XAI Interpreters (Diagram 4) for explainability and the Data Storage (Diagram 5) for model artifacts.

SageMaker Inference Code

# Python SDK for SageMaker Inference
from sagemaker.predictor import Predictor
from sagemaker.serializers import JSONSerializer
from sagemaker.deserializers import JSONDeserializer

predictor = Predictor(
    endpoint_name='xai-model-endpoint',
    sagemaker_session=sagemaker_session,
    serializer=JSONSerializer(),
    deserializer=JSONDeserializer()
)

response = predictor.predict({
    'features': [0.5, 1.2, 3.4, 0.8]
})
                

4. XAI Interpreters Architecture

This diagram details the XAI Interpreters layer, which integrates SHAP and LIME for model explainability. Hosted on Lambda or ECS, interpreters process SageMaker inference outputs (Diagram 3) and generate explanations, which are visualized via React UI Components. This layer interacts with the Data Storage (Diagram 5) for caching explanations.

SHAP Explanation Example

# Python Code for SHAP Explanation
import shap
import xgboost
import numpy as np

model = xgboost.XGBClassifier().fit(X_train, y_train)
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)

# Generate feature importance
shap.summary_plot(shap_values, X_test, plot_type="bar")
                

5. Data and Model Storage Architecture

This diagram focuses on the Data and Model Storage layer, which includes DynamoDB for metadata, S3 for model artifacts and datasets, and ElastiCache for caching explanations. The XAI Compute Layer (Diagram 3) and XAI Interpreters (Diagram 4) interact with this layer, with encryption managed by KMS.

Data Model

# DynamoDB Schema for XAI Metadata
{
  "TableName": "XAIMetadata",
  "KeySchema": [
    { "AttributeName": "pk", "KeyType": "HASH" },  // "MODEL#123"
    { "AttributeName": "sk", "KeyType": "RANGE" }  // "METADATA"
  ],
  "GlobalSecondaryIndexes": [
    {
      "IndexName": "PredictionIndex",
      "KeySchema": [
        { "AttributeName": "prediction_id", "KeyType": "HASH" }
      ],
      "Projection": { "ProjectionType": "INCLUDE", "NonKeyAttributes": ["features", "explanation"] }
    }
  ]
}
                

Cache Strategy

6. Observability and Security Architecture

This diagram details the Observability and Security layer, which includes CloudWatch for metrics and logs, X-Ray for tracing XAI workflows, and CloudTrail for auditing. Security is enforced with IAM, VPC, and KMS, ensuring compliance for XAI workloads.

CloudWatch Alarm Configuration

Resources:
  HighExplanationLatencyAlarm:
    Type: AWS::CloudWatch::Alarm
    Properties:
      AlarmName: HighExplanationLatency
      AlarmDescription: Triggers when XAI explanation latency exceeds 200ms
      MetricName: ExplanationLatency
      Namespace: AWS/SageMaker
      Statistic: Average
      Period: 300
      EvaluationPeriods: 2
      Threshold: 200
      ComparisonOperator: GreaterThanThreshold
      Dimensions:
        - Name: EndpointName
          Value: XAIEndpoint
      AlarmActions:
        - !Ref SNSTopic
      TreatMissingData: notBreaching
  SNSTopic:
    Type: AWS::SNS::Topic
    Properties:
      TopicName: XAIPlatformAlerts
                

Security Controls