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Synopsis

Build AI systems that don’t just work — they win trust. 
In today’s fast-moving AI-powered world, accuracy isn’t enough. Whether you’re building models for healthcare, finance, retail, or enterprise software, one thing is clear: if users and stakeholders can’t understand your AI, they won’t trust it. 
Explainable AI is your practical, results-driven guide to building transparent, trustworthy, and human-friendly AI systems. Packed with real-world examples and proven techniques, this book shows you how to turn black-box models into clear, interpretable tools that users can believe in — and regulators can approve. 
What you’ll gain from this book: 
•	Tools like SHAP and LIME to explain predictions clearly 
•	Strategies to increase model transparency without sacrificing accuracy 
•	Step-by-step ways to meet compliance, reduce risk, and gain user buy-in 
•	Industry case studies that prove explainability is a competitive advantage 
Insights that make your AI smarter and more responsible 
If you want to build AI solutions that people trust, adopt, and love — this is the book that will show you how.

Durée: environ 3 heures (03:05:02)
Date de publication: 14/02/2026; Unabridged; Copyright Year: — Copyright Statment: —

• Science et Technologie