Introduction to Bias in Machine Learning, presented by Matthias Feys, CTO @ ML6 at the Trustworthy & Ethical AI conference on Feb. 13th

This document discusses fairness in machine learning models. It notes that there is no single best definition of fairness and explores different definitions like unawareness, group fairness, and individual fairness. It also summarizes approaches to address bias like removing sensitive variables from training data, enforcing equal treatment between groups, and treating similar individuals similarly. The document outlines sources of bias like sample, prejudicial, exclusion, and algorithmic bias and discusses strategies to reduce bias in data preprocessing, model training, and prediction postprocessing. It emphasizes that fairness can be improved but not optimized for all definitions simultaneously.