Ethical Considerations and Bias Detection in Artificial Intelligence/Machine Learning Applications
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https://doi.org/10.70593/978-93-7185-870-0
Keywords:
Machine Learning, Bias Detection, Ethics, Artificial Intelligence, Decision Making, Decision Support System, Data PrivacySynopsis
At a time when artificial intelligence (AI) and machine learning (ML) are used to make sensitive societal decisions such as the ones related to criminal justice, healthcare, finance, education, employment, algorithmic fairness and bias mitigation are among the most important but challenging issues at hand. The goal of this book is to provide a holistic view across various disciplines of the ethical base, detection methods, and technical measures for trustworthy AI systems. Starting from a solid foundation of statistical bias, transparency systems and fairness-aware ML models, this book methodically looks at state-of-the-art methodologies, where we highlight their shortcomings and introduce a unified model framework for detecting bias and transparent algorithms. Moving beyond technical diagnoses, it examines key sociotechnical and policy tools that are required to implement AI responsibly, providing guidance to researchers, engineers, policy makers, and organizational leaders. Literature review has been driven following the experimental case, the fairness trade-offs, intersectional bias, explainability and regulatory compliance are discussed in depth by the authors. This work underscores that fairness in automated decision-making systems depends not only on algorithmic accuracy, but also institutional will and stakeholder engagement. The chapters in this book function as both an academic primer and a resourceful handbook, transitioning readers through an ever-growing ethical AI terrain. Whether you are a data scientist building and deploying an algorithm that encourages ethical speech, or a regulator working to create and refine guidelines around such algorithms, this book provides you with both the tools and the understanding you need for ethical technology development and deployment.
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Published
10 July 2025
Copyright (c) 2025 Jayesh Rane, Reshma Amol Chaudhari, Nitin Liladhar Rane
Details about the available publication format: E-Book
E-Book
ISBN-13 (15)
978-93-7185-870-0
Details about the available publication format: Book (Paperback)
Book (Paperback)
ISBN-13 (15)
978-93-7185-142-8
How to Cite
Rane, J. ., Chaudhari, R. A. ., & Rane, N. L. . (2025). Ethical Considerations and Bias Detection in Artificial Intelligence/Machine Learning Applications. Deep Science Publishing. https://doi.org/10.70593/978-93-7185-870-0
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