aif360

FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)

Introduction to trusted AI. Learn to use fairness algorithms to reduce and mitigate bias in data and models with aif360 and explain models with aix360

Fair and explainable ML workshop

Trying out things on Kaggle's Titanic dataset

Responsible AI Masterclass (June 2024 Run)

This project investigates the relationship between data quality issues (data smells) and fairness in machine learning (ML) models. By applying various fairness metrics and bias mitigation algorithms to multiple datasets, we aim to answer critical questions regarding the ethical implications of poor data quality in AI systems.

Fairness in data, and machine learning algorithms is critical to building safe and responsible AI systems from the ground up by design. Both technical and business AI stakeholders are in constant pursuit of fairness to ensure they meaningfully address problems like AI bias. While accuracy is one metric for evaluating the accuracy of a machine le…

My Own Repository with workfolders and others such as Group Projects etc...

Demographic vision fairness & bias audit across 126 intersectional bins with BCa bootstrap confidence intervals

Predicting loan defaults using Random Forest with SMOTE, SHAP, LIME, and fairness evaluation via AIF360 and Fairlearn

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

Automated EU AI Act compliance tool for high-risk credit scoring AI. Five assessments (FRIA, Cybersecurity, XAI, Bias, Risk) from one questionnaire. Built with Neo4j, SHAP, IBM AIF360, FastAPI, and React.

“Bias and Fairness in AI Systems: Theoretical Analysis and Empirical Validation Through Reweighing Mitigation.” This research goes beyond theoretical discussion by providing empirical validation through a Python-based fairness assessment framework built using IBM’s AIF360 toolkit and scikit-learn.

Fairness Analysis in US Mortgage Lending with Machine Learning Algorithms

Visualising Accuracy vs. Fairness in ML models using AIF360 tools and dataset

Fairness-check op de Adult-dataset met IBM AIF360: detectie (Disparate Impact) + mitigatie (Reweighing)

An ML-based healthcare fairness platform that compares healthcare utilization predictions before and after bias mitigation using Logistic Regression, AIF360 reweighing, and fairness metrics on the MEPS HC-181 dataset. Includes a Flask API and Streamlit interface.

A Python toolkit for detecting and mitigating ethical bias in machine learning models. This project provides wrapper classes around IBM's AI Fairness 360 (AIF360) library to make bias detection and mitigation more accessible and easier to implement in machine learning pipelines.

Replication of bias mitigation experiments from arXiv:2412.09896 - Fairlearn vs AIF360 on the Adult Income dataset with XGBoost