Fair Machine Learning with R: Detecting and Reducing Algorithmic Bias

By BROOKS SAINT

FAIR MACHINE LEARNING WITH R: Detecting and Reducing Algorithmic Bias

Bias in machine learning isn't rare it's built into the data, the models, and the decisions they produce. If you're not actively measuring and correcting it, your system is already biased.

This book shows how to fix that practically, systematically, and with real-world workflows using R.

Instead of theory-heavy explanations, this guide focuses on how bias actually enters machine learning systems, how to measure it with precision, and how to reduce it using proven techniques across the entire pipeline. From data preparation to deployment, every step is designed to help you build models that are not just accurate but accountable.

You'll learn how to move beyond surface-level metrics and expose hidden disparities, apply fairness constraints during model training, and correct biased decisions without rebuilding your system from scratch.

Inside this book, you'll learn how to:

Detect bias in datasets, features, and model outputsMeasure fairness using statistical and error-based metrics in RVisualize disparities so they are clear and actionableApply pre-processing, in-processing, and post-processing techniquesBuild fairness-aware machine learning pipelines from end to endUse interpretability tools to uncover hidden biasAudit and monitor models in production environmentsImplement real-world case studies across finance, healthcare, hiring, and more

This book is for:

Data scientists and analysts using RMachine learning engineers building real-world systemsResearchers working on ethical AI and responsible data scienceProfessionals who need to understand and control algorithmic bias

What makes this book different:

Focused on practical implementation not abstract theoryCovers the full lifecycle from raw data to deployed systemEmphasizes real-world trade-offs between accuracy and fairnessBuilt specifically for R workflows, not generic pseudocode

If your model makes decisions that affect real people, fairness is not optional.

This book shows you how to build systems that stand up to scrutiny and actually work in the real world.

• Format: Paperback
• Language: English
• ISBN: B0GV82JGF9
• ISBN13: 9798253955949
• Release Date: March 2026
• Publisher: Independently Published
• Length: 124 Pages
• Weight: 0.39 lbs.
• Dimensions: 0.3" x 6.0" x 9.0"