AI Governance Watchdog

Shining light on AI’s black box — for fair, transparent decisions.

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Inspiration

AI models are increasingly making high-impact decisions — from loan approvals to hiring recommendations. Yet, most systems operate as black boxes. Detecting bias or ethical risks usually involves slow, manual audits.

That’s why we built AI Governance Watchdog — an intelligent agent that continuously monitors and audits AI models for fairness, bias, and compliance.

What it does

AI Governance Watchdog automates the auditing of machine learning models hosted on Amazon SageMaker, including custom models.
It leverages Amazon Bedrock for reasoning and AgentCore for orchestration, producing human-readable audit reports stored in S3 and visualized through a modern React dashboard.

Key features:

  • Run bias and fairness audits automatically or on schedule
  • Audit both default and custom SageMaker models
  • Generate detailed, explainable audit reports
  • View metrics on a clean, visual dashboard

How we built it

We combined AWS services to make a self-sustaining governance agent:

  • Amazon Bedrock – Ethical reasoning and audit report generation
  • Amazon SageMaker – Model hosting and data inference (custom model support)
  • AWS Lambda + API Gateway – Backend logic and API exposure
  • Amazon S3 + DynamoDB – Report and metadata storage
  • Amazon Cognito – Authentication layer
  • React + Recharts – Frontend visualization
  • CloudFormation Template – One-click deployment

Challenges we ran into

  • Making Bedrock reasoning models output structured, explainable results
  • Keeping the audit process fully autonomous across AWS services
  • Building consistent bias metrics for custom model endpoints
  • Real-time dashboard updates without overloading APIs

Accomplishments that we're proud of

  • Built an autonomous AWS AI governance agent end-to-end
  • Enabled plug-and-play auditing for any SageMaker model
  • Created a visual bias analytics dashboard
  • Reduced human effort in compliance monitoring

What we learned

  • How to orchestrate multiple AWS AI services efficiently
  • Practical techniques for explainable AI reasoning
  • The importance of structured outputs for AI audit traceability
  • How automation turns governance into continuous ethical assurance

What's next for AI Governance Watchdog

  • Add real-time bias drift detection and alerting
  • Expand to multi-cloud AI governance (Azure, GCP)
  • Offer a REST API for continuous integration (CI/CD) audits
  • Integrate fine-tuned Bedrock models for domain-specific fairness checks
  • Enable natural language queries like “Show me last week’s bias report”

“Turning compliance into continuous intelligence.”

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