9781264922444 - Mitigating Bias in Machine Learning de Berry, Carlotta A.; Marshall, Brandeis Hill (18 resultados)

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Paperback. Condición: New. This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries.Edited by engineering and computing experts, Mitigating Bias in Machine Learning includes contributions from recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning practices and clearly show how they were reduced.Mitigating Bias in Machine Learning addresses:Ethical and Societal Implications of Machine LearningSocial Media and Health Information DisseminationComparative Case Study of Fairness ToolkitsBias Mitigation in Hate Speech DetectionUnintended Systematic Biases in Natural Language Processing Combating Bias in Large Language ModelsRecognizing Bias in Medical Machine Learning and AI ModelsMachine Learning Bias in HealthcareAchieving Systemic Equity in Socioecological SystemsCommunity Engagement for Machine Learning.

Paperback. Condición: new. Paperback. This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries.Edited by engineering and computing experts, Mitigating Bias in Machine Learning includes contributions from recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning practices and clearly show how they were reduced.Mitigating Bias in Machine Learning addresses:Ethical and Societal Implications of Machine LearningSocial Media and Health Information DisseminationComparative Case Study of Fairness ToolkitsBias Mitigation in Hate Speech DetectionUnintended Systematic Biases in Natural Language Processing Combating Bias in Large Language ModelsRecognizing Bias in Medical Machine Learning and AI ModelsMachine Learning Bias in HealthcareAchieving Systemic Equity in Socioecological SystemsCommunity Engagement for Machine Learning Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condición: New. This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries.Edited by engineering and computing experts, Mitigating Bias in Machine Learning includes contributions from recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning practices and clearly show how they were reduced.Mitigating Bias in Machine Learning addresses:Ethical and Societal Implications of Machine LearningSocial Media and Health Information DisseminationComparative Case Study of Fairness ToolkitsBias Mitigation in Hate Speech DetectionUnintended Systematic Biases in Natural Language Processing Combating Bias in Large Language ModelsRecognizing Bias in Medical Machine Learning and AI ModelsMachine Learning Bias in HealthcareAchieving Systemic Equity in Socioecological SystemsCommunity Engagement for Machine Learning.

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Paperback. Condición: new. Paperback. This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries.Edited by engineering and computing experts, Mitigating Bias in Machine Learning includes contributions from recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning practices and clearly show how they were reduced.Mitigating Bias in Machine Learning addresses:Ethical and Societal Implications of Machine LearningSocial Media and Health Information DisseminationComparative Case Study of Fairness ToolkitsBias Mitigation in Hate Speech DetectionUnintended Systematic Biases in Natural Language Processing Combating Bias in Large Language ModelsRecognizing Bias in Medical Machine Learning and AI ModelsMachine Learning Bias in HealthcareAchieving Systemic Equity in Socioecological SystemsCommunity Engagement for Machine Learning Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Paperback. Condición: New. This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries.Edited by engineering and computing experts, Mitigating Bias in Machine Learning includes contributions from recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning practices and clearly show how they were reduced.Mitigating Bias in Machine Learning addresses:Ethical and Societal Implications of Machine LearningSocial Media and Health Information DisseminationComparative Case Study of Fairness ToolkitsBias Mitigation in Hate Speech DetectionUnintended Systematic Biases in Natural Language Processing Combating Bias in Large Language ModelsRecognizing Bias in Medical Machine Learning and AI ModelsMachine Learning Bias in HealthcareAchieving Systemic Equity in Socioecological SystemsCommunity Engagement for Machine Learning.

Paperback. Condición: new. Paperback. This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries.Edited by engineering and computing experts, Mitigating Bias in Machine Learning includes contributions from recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning practices and clearly show how they were reduced.Mitigating Bias in Machine Learning addresses:Ethical and Societal Implications of Machine LearningSocial Media and Health Information DisseminationComparative Case Study of Fairness ToolkitsBias Mitigation in Hate Speech DetectionUnintended Systematic Biases in Natural Language Processing Combating Bias in Large Language ModelsRecognizing Bias in Medical Machine Learning and AI ModelsMachine Learning Bias in HealthcareAchieving Systemic Equity in Socioecological SystemsCommunity Engagement for Machine Learning Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condición: New. This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries.Edited by engineering and computing experts, Mitigating Bias in Machine Learning includes contributions from recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning practices and clearly show how they were reduced.Mitigating Bias in Machine Learning addresses:Ethical and Societal Implications of Machine LearningSocial Media and Health Information DisseminationComparative Case Study of Fairness ToolkitsBias Mitigation in Hate Speech DetectionUnintended Systematic Biases in Natural Language Processing Combating Bias in Large Language ModelsRecognizing Bias in Medical Machine Learning and AI ModelsMachine Learning Bias in HealthcareAchieving Systemic Equity in Socioecological SystemsCommunity Engagement for Machine Learning.