Bias in the Machine
Understanding and Mitigating Algorithmic Bias in Neural Networks and AI Systems
Imagine deploying an AI system that makes decisions affecting millions of people—hiring candidates, approving loans, or diagnosing medical conditions—only to discover it systematically disadvantages certain groups. This is the reality of algorithmic bias, and it's more common than you might think. In neural networks and machine learning systems, bias doesn't emerge from malicious intent; it quietly infiltrates through training data, design choices, and evaluation methods. This book demystifies how bias enters AI systems and, more importantly, shows you how to stop it. You'll explore the technical mechanisms that allow bias to propagate through feedforward neural networks, examine real-world case studies where bias caused tangible harm, and master practical strategies to detect and mitigate it. Whether you're building models, managing AI teams, or making decisions about AI deployment, you'll gain the frameworks and tools needed to create fairer, more responsible AI systems that serve everyone equitably.
Est. Length
222 pages (10 chapters)
Language
English
Publication date
Jan 31, 2026
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