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

Trying out things on Kaggle's Titanic dataset

Responsible AI Masterclass (June 2024 Run)

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…

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.

Fair and explainable ML workshop

Fairness Analysis in US Mortgage Lending with Machine Learning Algorithms

This repository contains the notebooks used to produce the data, figures, and table-data included in the bachelor's thesis "Evaluation of automatic bias detection and pre-processing mitigation techniques" by Max Kleinegger.

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

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

This repo contains two jupyter notebooks that demonstrates how to identify and correct algorithmic bias in machine learning models using Python and Julia.

This notebook represents my personal code, notes, and reflections for the Manning liveProject titled "Mitigate Machine Learning Bias: Shap and AIF360" by Michael McKenna. Any citations or references to original course material retain the original author copyright and ownership. Personal code is licensed under the MIT License.

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.

Building Fair AI models tutorial at PyData Berlin / REVISION 2018

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

Gender classification model that uses a CNN to classify images of faces as male or female. The notebook includes code for data preprocessing, model architecture, training, and evaluation which will then be used for algorithmic bias detection.

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.