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.
