AI Red Team Operations Console
A structured audit framework for ML pipelines. 14 stages, 100+ checks, and every leakage pattern that silent model failures are made of.
Autonomous Update Review Architecture for Federated Learning. A full-stack platform for inspecting, scoring, and auditing model updates using SHAP explainability, anomaly detection, and ledger-based tracking. Built with FastAPI, React 19, and Flower.
A production-grade LLM Evaluation & Benchmarking Framework for systematic model auditing. Features parallel benchmarking, fairness/bias detection, MMLU integration, and a real-time analytics dashboard powered by React and FastAPI.
Reproducible pipeline for silent-failure auditing in ECG accept-sets (MIT-BIH) with Newton–Puiseux onset scoring
Production-grade platform for auditing AI/ML models for fairness, explainability, and robustness. JWT auth, SHA-256 model verification, paginated REST API, dark web dashboard. 30/30 tests passing.
SHAP-based bias detection and model interpretability auditing framework. Feature importance analysis and ML audit automation.
Coordinate multiple LLMs in parallel for voting, debate, synthesis, critique, red teaming, and verification via MCP and Claude Code
Add a description, image, and links to the model-auditing topic page so that developers can more easily learn about it.
To associate your repository with the model-auditing topic, visit your repo's landing page and select "manage topics."