menu
{ "item_title" : "Explainable AI (Xai)", "item_author" : [" Sadie Graham Michael Ward "], "item_description" : "Introduces the fundamental concepts of explainable and interpretable AI. Provides a clear distinction between black-box, glass-box, and hybrid AI models. Covers popular XAI techniques, including feature importance, LIME, SHAP, counterfactuals, and surrogate models. Examines applications of XAI across healthcare, finance, agriculture, governance, cybersecurity, and education. Discusses ethical concerns, bias detection, fairness, and accountability in AI systems. Explores the role of XAI in regulatory compliance and trustworthy AI frameworks. Integrates real-world case studies to demonstrate practical implementation. Serves as a valuable resource for students, researchers, policymakers, and industry professionals.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/1/78/715/610/1787156109_b.jpg", "price_data" : { "retail_price" : "142.99", "online_price" : "142.99", "our_price" : "142.99", "club_price" : "142.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Explainable AI (Xai)|Sadie Graham Michael Ward

Explainable AI (Xai)

local_shippingShip to Me
In Stock.
FREE Shipping for Club Members help

Overview

Introduces the fundamental concepts of explainable and interpretable AI. Provides a clear distinction between black-box, glass-box, and hybrid AI models. Covers popular XAI techniques, including feature importance, LIME, SHAP, counterfactuals, and surrogate models. Examines applications of XAI across healthcare, finance, agriculture, governance, cybersecurity, and education. Discusses ethical concerns, bias detection, fairness, and accountability in AI systems. Explores the role of XAI in regulatory compliance and trustworthy AI frameworks. Integrates real-world case studies to demonstrate practical implementation. Serves as a valuable resource for students, researchers, policymakers, and industry professionals.

This item is Non-Returnable

Details

  • ISBN-13: 9781787156104
  • ISBN-10: 1787156109
  • Publisher: Kruger Brentt Publisher Uk. Ltd.
  • Publish Date: January 2026
  • Dimensions: 10 x 7 x 0.88 inches
  • Shipping Weight: 1.94 pounds
  • Page Count: 274

Related Categories

You May Also Like...

    1

BAM Customer Reviews