AI Bias Mitigation for Fair Coding Practices : Ensuring Fairness in Machine Learning Models with Ethical Case Studies

Take control of AI fairness and build trustworthy machine learning models with AI Bias Mitigation for Fair Coding Practices: Ensuring Fairness in Machine Learning Models with Ethical Case Studies by Brenden Jay. In an age where AI decisions impact lives—from hiring algorithms to loan approvals—this essential guide equips you with the tools to detect, measure, and eliminate bias, ensuring ethical and equitable outcomes in your projects.
Struggling with skewed datasets, unfair predictions, or regulatory scrutiny? This book offers a clear path forward, blending theory with action. Author Brenden Jay draws on real-world expertise to cover bias origins, fairness metrics, preprocessing techniques (resampling, reweighting), in-process methods (adversarial debiasing, fairness constraints), post-processing adjustments, and explainability tools like SHAP and LIME. Dive into 15 practical case studies—spanning recruitment bias, facial recognition, credit scoring, and healthcare diagnostics—each with actionable code, data examples, and ethical reflections to guide your work.
Explore hands-on exercises using Python and popular libraries (scikit-learn, TensorFlow, Fairlearn), supplemented by a companion GitHub repository with datasets, scripts, and resources. Learn to audit models, comply with laws like GDPR and CCPA, and adopt best practices for diverse teams. Appendices provide a fairness glossary, legal overview, and setup guides, making this accessible for data scientists, ML engineers, or ethicists with basic Python and ML knowledge.