Build machine learning models that are ethical, fair, and unbiased. In ML Ethics , you'll learn how to design fair and unbiased machine learning algorithms that deliver reliable and transparent results. As machine learning continues to impact industries and societies, it's essential to address bias and fairness during the model-building process. This book provides you with the knowledge and tools to develop ethical AI systems that are transparent, inclusive, and accountable. Inside, you'll learn how to: ...
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Build machine learning models that are ethical, fair, and unbiased. In ML Ethics , you'll learn how to design fair and unbiased machine learning algorithms that deliver reliable and transparent results. As machine learning continues to impact industries and societies, it's essential to address bias and fairness during the model-building process. This book provides you with the knowledge and tools to develop ethical AI systems that are transparent, inclusive, and accountable. Inside, you'll learn how to: Understand the key ethical challenges in machine learning, including bias , discrimination , accountability , and privacy concerns. Explore common sources of bias in data and algorithms, such as sampling bias , label bias , and algorithmic bias , and how they affect model outcomes. Use fairness metrics and techniques like demographic parity , equalized odds , and disparate impact analysis to assess and improve model fairness. Implement methods for bias mitigation in pre-processing, in-processing, and post-processing stages to create more equitable models. Learn how to audit algorithms for bias using real-world datasets and fairness testing tools like Fairness Indicators and AIF360 . Apply transparency and explainability techniques to make machine learning models more interpretable, using tools like LIME , SHAP , and Counterfactual Explanations . Address privacy issues in ML, implementing techniques such as differential privacy , federated learning , and secure multiparty computation to protect user data. Explore the social implications of machine learning, including the impact on marginalized groups, employment, and data ownership. Integrate ethical AI guidelines and frameworks into your ML pipeline to ensure responsible decision-making. Work with real-world examples and case studies in finance, healthcare, criminal justice, and hiring to understand the ethical implications of AI deployment. Packed with hands-on case studies , tools for fairness auditing , and actionable guidelines , this book will help you build ethical ML models that promote fairness, reduce bias, and empower trust in AI systems. Who This Book Is For Machine learning engineers and data scientists interested in ensuring fairness and reducing bias in their models AI researchers and ethics professionals focusing on responsible AI and data science practices Students and educators looking to explore the ethical considerations in machine learning Developers working in sectors like finance , healthcare , criminal justice , and recruitment where fairness is crucial Business leaders and policy makers who need to understand and mitigate AI bias and fairness issues in the workplace Build machine learning models that are not only accurate but also ethical, fair, and transparent.
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Add this copy of ML Ethics Ensuring Fair and Unbiased Algorithms: to cart. $13.68, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2025 by Independently Published.
