{"product_id":"fair-machine-learning-with-r-detecting-and-reducing-algorithmic-bias-9798253955949","title":"Fair Machine Learning with R: Detecting and Reducing Algorithmic Bias","description":"\u003cp\u003e • Author(s): Brooks Saint\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Statistics\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eFAIR MACHINE LEARNING WITH R: \u003c\/b\u003e\u003cb\u003eDetecting and Reducing Algorithmic Bias\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eBias 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.\u003c\/p\u003e\u003cp\u003eThis book shows how to fix that practically, systematically, and with real-world workflows using R.\u003c\/p\u003e\u003cp\u003eInstead 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.\u003c\/p\u003e\u003cp\u003eYou'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.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eInside this book, you'll learn how to: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eDetect bias in datasets, features, and model outputs\u003c\/li\u003e\n\u003cli\u003eMeasure fairness using statistical and error-based metrics in R\u003c\/li\u003e\n\u003cli\u003eVisualize disparities so they are clear and actionable\u003c\/li\u003e\n\u003cli\u003eApply pre-processing, in-processing, and post-processing techniques\u003c\/li\u003e\n\u003cli\u003eBuild fairness-aware machine learning pipelines from end to end\u003c\/li\u003e\n\u003cli\u003eUse interpretability tools to uncover hidden bias\u003c\/li\u003e\n\u003cli\u003eAudit and monitor models in production environments\u003c\/li\u003e\n\u003cli\u003eImplement real-world case studies across finance, healthcare, hiring, and more\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cb\u003eThis book is for: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eData scientists and analysts using R\u003c\/li\u003e\n\u003cli\u003eMachine learning engineers building real-world systems\u003c\/li\u003e\n\u003cli\u003eResearchers working on ethical AI and responsible data science\u003c\/li\u003e\n\u003cli\u003eProfessionals who need to understand and control algorithmic bias\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cb\u003eWhat makes this book different: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eFocused on practical implementation not abstract theory\u003c\/li\u003e\n\u003cli\u003eCovers the full lifecycle from raw data to deployed system\u003c\/li\u003e\n\u003cli\u003eEmphasizes real-world trade-offs between accuracy and fairness\u003c\/li\u003e\n\u003cli\u003eBuilt specifically for R workflows, not generic pseudocode\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eIf your model makes decisions that affect real people, fairness is not optional.\u003c\/p\u003e\u003cp\u003eThis book shows you how to build systems that stand up to scrutiny and actually work in the real world.\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47775465734295,"sku":"9798253955949","price":1725.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798253955949.webp?v=1777990527","url":"https:\/\/atlanticbooks.com\/products\/fair-machine-learning-with-r-detecting-and-reducing-algorithmic-bias-9798253955949","provider":"Atlantic Books","version":"1.0","type":"link"}