Description

Book Synopsis

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 Learning
  • Social Media and Health Information Dissemination

Mitigating Bias in Machine Learning

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    £42.74

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    RRP £44.99 – you save £2.25 (5%)

    Order before 4pm today for delivery by Fri 31 Jul 2026.

    A Paperback by Carlotta A. Berry

    20 in stock

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      Publisher: McGraw-Hill Education
      Publication Date: Publication Date: 9/22/2024
      ISBN13: 9781264922444, 978-1264922444
      ISBN10: 1264922442
      Also in:
      Technology, Engineering & Agriculture Electronics and communications engineering

      Description

      Book Synopsis

      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 Learning
      • Social Media and Health Information Dissemination

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