Explainable AI
Making Machine Intelligence Transparent, Trustworthy, and Understandable for Human Decision-Making
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Explainable AI
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ナレーター：
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Maha Amir
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著者：
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Sam Miley
このコンテンツについて
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.
©2025 Sam Miley (P)2025 Sam Miley