List of Topics:
Location Research Breakthrough Possible @S-Logix pro@slogix.in

Office Address

Social List

Explainable AI (XAI) Models for Decision Support Using Google Cloud AI Platform

XAI

AI (XAI) Models for Decision Support Using Google Cloud

  • Use Case : Organizations in healthcare, finance, and regulatory sectors rely on AI models for critical decisions. However, black-box models are hard to trust and interpret. Explainable AI (XAI) provides transparency, accountability, and actionable insights behind predictions, enabling stakeholders to understand why a model made a specific decision.

Objective

  • Build AI models that are interpretable and explainable to humans.

    Support decision-making in critical domains with justifiable AI predictions.

    Integrate Google Cloud AI tools to automate explanations, monitor fairness, and validate model predictions.

    Ensure regulatory compliance and improve stakeholder trust in AI systems.

Project Description

  • This project develops a decision-support system using XAI principles:

    Data Collection: Gather structured and unstructured data (e.g., patient records, financial transactions, customer data).

    Model Training: Train ML models (classification, regression, or deep learning) using Vertex AI or AutoML.

    Explainability Integration: Use SHAP, LIME, or Google Cloud Explainable AI to generate interpretable insights on predictions.

    Decision Support Dashboard: Visualize feature importance, prediction confidence, and counterfactual scenarios for decision-makers.

    Monitoring & Feedback: Continuously monitor model performance, fairness, and biases, retraining when necessary.

Key Technologies & Google Cloud Platform Services

  • GCP Service Purpose
    Cloud Storage Stores datasets (structured & unstructured) used for training XAI models.
    Vertex AI Train, deploy, and manage ML models; supports integration with XAI methods for model explanations.
    Explainable AI (Vertex AI XAI) Provides feature attributions, model interpretability, and explanations for predictions.
    BigQuery Store and query large datasets; supports feature engineering and analytics for model insights.
    Dataflow Preprocess data pipelines (cleaning, transforming, and feature extraction) for ML training.
    AI Platform Pipelines Orchestrate end-to-end workflows including preprocessing, training, evaluation, and explainability.
    Looker / Data Studio Visualize explanations, feature importance, prediction confidence, and decision insights for end users.
    Cloud Functions Automate triggers for generating explanations or alerts when new predictions occur.
    Cloud Monitoring / Logging Track model performance, prediction latency, and operational health.
    Cloud Key Management Service (KMS) Encrypt sensitive datasets for privacy and compliance.