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

Personal Data Science Projects

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

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

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

Fairlearn Jupyter notebooks samples

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

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.

Evaluating Fairness in Machine Learning: Comparative Analysis of Fairlearn and AIF360

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

Full-stack Data Science engine: Advanced SQL feature engineering, Customer Segmentation (K-Means), and LTV Prediction using Deep Learning (TensorFlow). Includes automated AI Ethics & Bias auditing.

This repository was used for my thesis. The goal was to find a biased dataset, and mitigate its bias. That is done under the patients directory. Check the README file for more.

A demonstration of detecting and mitigating bias in AI.

Mitigating bias for our model used to identify Glioblastoma Multiforme (tumors) using Xgboost.

Student Success Model (SSM)

Investigation of gender bias in music recommender systems

Credit risk scoring model using XGBoost with fairness constraints implemented via Fairlearn to mitigate bias in financial decision making. Implemented on the Statlog (German Credit Data) dataset. Also includes the notebooks for the preprocessing and the hyper tuning of the dataset.

A comprehensive fairness analysis of the Boston Residents Jobs Policy compliance data using Fairlearn to detect and mitigate bias in construction project employment