fairlearn

Auditing algorithmic bias in criminal justice, hiring, lending, healthcare, welfare, and tenant screening: 7 open-source audits, measurable fairness gaps, and concrete fixes.

🥉 3rd Place Hackathon Winner | Solo-built AI platform for detecting, explaining, and mitigating bias in machine learning systems.

Personal Data Science Projects

Demos about Teaching your Models to play fair with FairLearn

Assess fairness of machine learning models and choose an appropriate fairness metric for your use case with Fairlearn

An end-to-end MLOps pipeline for a production-grade fraud detection model. This project demonstrates best practices including data versioning (DVC), experiment tracking (MLflow), CI/CD (GitHub Actions), containerization (Docker), deployment on GKE, and advanced model analysis (poisoning attacks, drift, fairness, explainability).

Learn different techniques for mitigating fairness-related harms using Fairlearn.

Drop-in encrypted Fairlearn metrics over CKKS. Same API surface; ciphertext arithmetic via TenSEAL or OpenFHE.

Microsoft Ignite - Getting started on your health-tech journey using responsible AI

🔬 Drop in any ML model → get SHAP explainability, fairness audit & drift detection in seconds

Demo's of FairLearn and InterpretML as described in my article on responsible AI.

AI-powered bias detection for datasets and ML models — with fairness metrics, natural language reports, and explainability tools.

Fairlearn Jupyter notebooks samples

AI-powered bias auditing tool that detects demographic disparities in datasets and explains findings in plain English using Gemini.

Project: Credit XAI – Ethical AI in Lending & Red Teaming AI Credit Underwriting

A platform developed with Cash App to help ML engineers detect and visualize biases in models using Fairlearn. Features include a collaborative and interactive dashboard (React, Chart.js), a Flask backend, and a secure MySQL database for data storage and analysis.

Human-in-the-loop credit decisioning platform with SHAP explainability, Fairlearn bias auditing, MLflow tracking, and a counterfactual recourse engine that tells declined applicants exactly what to change to get approved.

An ethically-aware deep learning project to predict credit card offer acceptance while mitigating income-based bias using SHAP, Fairlearn, and AIF360.

Exploratory bias and fairness diagnostics for tabular datasets using Fairlearn and scikit-learn. Streamlit app with a synthetic demo only — not a fairness verdict or compliance determination.

Responsible AI portfolio: bias auditing, AI governance, and ethical multi-agent systems.