{"product_id":"ml-ethics-ensuring-fair-and-unbiased-algorithms-address-bias-and-fairness-in-machine-learning-design-9798264843846","title":"ML Ethics Ensuring Fair and Unbiased Algorithms: Address bias and fairness in machine learning design","description":"\u003cp\u003e • Author(s): Isandro Myles\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Machine Theory\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eBuild machine learning models that are ethical, fair, and unbiased.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eIn \u003cb\u003eML Ethics\u003c\/b\u003e, you'll learn how to design \u003cb\u003efair\u003c\/b\u003e and \u003cb\u003eunbiased\u003c\/b\u003e machine learning algorithms that deliver reliable and transparent results. As machine learning continues to impact industries and societies, it's essential to address \u003cb\u003ebias\u003c\/b\u003e and \u003cb\u003efairness\u003c\/b\u003e during the model-building process. This book provides you with the knowledge and tools to develop \u003cb\u003eethical AI\u003c\/b\u003e systems that are transparent, inclusive, and accountable.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eInside, you'll learn how to: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eUnderstand the key ethical challenges\u003c\/b\u003e in machine learning, including \u003cb\u003ebias\u003c\/b\u003e, \u003cb\u003ediscrimination\u003c\/b\u003e, \u003cb\u003eaccountability\u003c\/b\u003e, and \u003cb\u003eprivacy\u003c\/b\u003e concerns.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eExplore common sources of \u003cb\u003ebias\u003c\/b\u003e in data and algorithms, such as \u003cb\u003esampling bias\u003c\/b\u003e, \u003cb\u003elabel bias\u003c\/b\u003e, and \u003cb\u003ealgorithmic bias\u003c\/b\u003e, and how they affect model outcomes.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eUse \u003cb\u003efairness metrics\u003c\/b\u003e and techniques like \u003cb\u003edemographic parity\u003c\/b\u003e, \u003cb\u003eequalized odds\u003c\/b\u003e, and \u003cb\u003edisparate impact analysis\u003c\/b\u003e to assess and improve model fairness.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eImplement methods for \u003cb\u003ebias mitigation\u003c\/b\u003e in pre-processing, in-processing, and post-processing stages to create more equitable models.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eLearn how to \u003cb\u003eaudit algorithms\u003c\/b\u003e for bias using real-world datasets and fairness testing tools like \u003cb\u003eFairness Indicators\u003c\/b\u003e and \u003cb\u003eAIF360\u003c\/b\u003e.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eApply \u003cb\u003etransparency and explainability\u003c\/b\u003e techniques to make machine learning models more interpretable, using tools like \u003cb\u003eLIME\u003c\/b\u003e, \u003cb\u003eSHAP\u003c\/b\u003e, and \u003cb\u003eCounterfactual Explanations\u003c\/b\u003e.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eAddress \u003cb\u003eprivacy issues\u003c\/b\u003e in ML, implementing techniques such as \u003cb\u003edifferential privacy\u003c\/b\u003e, \u003cb\u003efederated learning\u003c\/b\u003e, and \u003cb\u003esecure multiparty computation\u003c\/b\u003e to protect user data.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eExplore the \u003cb\u003esocial implications\u003c\/b\u003e of machine learning, including the impact on marginalized groups, employment, and data ownership.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eIntegrate \u003cb\u003eethical AI\u003c\/b\u003e guidelines and frameworks into your ML pipeline to ensure responsible decision-making.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eWork with \u003cb\u003ereal-world examples\u003c\/b\u003e and \u003cb\u003ecase studies\u003c\/b\u003e in finance, healthcare, criminal justice, and hiring to understand the ethical implications of AI deployment.\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003ePacked with \u003cb\u003ehands-on case studies\u003c\/b\u003e, \u003cb\u003etools for fairness auditing\u003c\/b\u003e, and \u003cb\u003eactionable guidelines\u003c\/b\u003e, this book will help you build \u003cb\u003eethical ML models\u003c\/b\u003e that promote fairness, reduce bias, and empower trust in AI systems.\u003c\/p\u003e\u003cb\u003eWho This Book Is For\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eMachine learning engineers\u003c\/b\u003e and \u003cb\u003edata scientists\u003c\/b\u003e interested in ensuring fairness and reducing bias in their models\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eAI researchers\u003c\/b\u003e and \u003cb\u003eethics professionals\u003c\/b\u003e focusing on responsible AI and data science practices\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eStudents\u003c\/b\u003e and \u003cb\u003eeducators\u003c\/b\u003e looking to explore the ethical considerations in machine learning\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eDevelopers\u003c\/b\u003e working in sectors like \u003cb\u003efinance\u003c\/b\u003e, \u003cb\u003ehealthcare\u003c\/b\u003e, \u003cb\u003ecriminal justice\u003c\/b\u003e, and \u003cb\u003erecruitment\u003c\/b\u003e where fairness is crucial\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eBusiness leaders\u003c\/b\u003e and \u003cb\u003epolicy makers\u003c\/b\u003e who need to understand and mitigate AI bias and fairness issues in the workplace\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cb\u003eBuild machine learning models that are not only accurate but also ethical, fair, and transparent.\u003c\/b\u003e\u003c\/p\u003e","brand":"Atlantic 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