Data Ethics with R: Fairness, Bias Detection, and Trustworthy Analytics

By WALTON BRYANT

DATA ETHICS WITH R: FAIRNESS, BIAS DETECTION, AND TRUSTWORTHY ANALYTICS
DETECT BIAS, PROTECT PRIVACY, AND BUILD RESPONSIBLE MODELS

Most data science books teach you how to build models.
This one teaches you how to build models that don't break trust, trigger risk, or fail under real-world scrutiny.

If you're working with data in R and deploying models that influence decisions credit, healthcare, hiring, risk scoring then accuracy alone is not enough. Hidden bias, poor data practices, and lack of transparency can destroy the value of your system, no matter how good the metrics look.

This book is built for practitioners who want to move beyond theory and actually implement ethical, audit-ready data systems.

Inside, you'll learn how to:

Detect and measure bias using real R workflows and fairness metricsClean and prepare data without introducing hidden distortionsBuild models that balance performance with fairnessApply explainability tools like SHAP and LIME for real audit scenariosImplement privacy techniques including differential privacy and data maskingDesign reproducible, traceable pipelines that stand up to regulatory reviewMonitor deployed models for drift, bias, and system failure in real time

This is not a compliance checklist. It's a system-level approach to building data products that last.

Whether you're a data scientist, analyst, machine learning engineer, or technical decision-maker, this book shows you how to turn ethical data practices into a competitive advantage by building systems that are not just accurate, but trustworthy, transparent, and defensible.

If your models are making decisions that matter, this book will show you how to build them the right way.

• Format: Paperback
• Language: English
• ISBN: B0GTVKL4HT
• ISBN13: 9798253551967
• Release Date: March 2026
• Publisher: Independently Published
• Length: 136 Pages
• Weight: 0.42 lbs.
• Dimensions: 0.3" x 6.0" x 9.0"