Overview
Learn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers.- Review the different ways of making an AI model interpretable and explainable
- Examine the biasness and good ethical practices of AI models
- Quantify, visualize, and estimate reliability of AI models
- Design frameworks to unbox the black-box models
- Assess the fairness of AI models
- Understand the building blocks of trust in AI models
- Increase the level of AI adoption
The book, Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks [Bulk, Wholesale, Quantity] ISBN#9781484271575 in Paperback by Pradeepta Mishra may be ordered in bulk quantities. Minimum starts at 25 copies. Availability based on publisher status and quantity being ordered.
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