Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks

  • 3h 27m
  • Pradeepta Mishra
  • Apress
  • 2021

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.

You'll begin with an introduction to model explainability and interpretability basics, ethical consideration, and biases in predictions generated by AI models. Next, you'll look at methods and systems to interpret linear, non-linear, and time-series models used in AI. The book will also cover topics ranging from interpreting to understanding how an AI algorithm makes a decision

Further, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, you will be introduced to model explainability for unstructured data and natural language processing–related tasks. Additionally, the book looks at counterfactual explanations for AI models. Practical Explainable AI Using Python shines the light on deep learning models, rule-based expert systems, and computer vision tasks using various XAI frameworks.

You will:

  • 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

About the Author

Pradeepta Mishra is the Head of AI (Leni) at L&T Infotech (LTI), leading a large group of data scientists, computational linguistics experts, machine learning and deep learning experts in building next generation product, ‘Leni’ world’s first virtual data scientist. He was awarded as "India's Top - 40Under40DataScientists" by Analytics India Magazine. He is an author of 4 books, his first book has been recommended in HSLS center at the University of Pittsburgh, PA, USA. His latest book #PytorchRecipes was published by Apress. He has delivered a keynote session at the Global Data Science conference 2018, USA. He has delivered a TEDx talk on "Can Machines Think?", available on the official TEDx YouTube channel. He has delivered 200+ tech talks on data science, ML, DL, NLP, and AI in various Universities, meetups, technical institutions and community arranged forums.

In this Book

  • Introduction
  • Model Explainability and Interpretability
  • AI Ethics, Biasness, and Reliability
  • Explainability for Linear Models
  • Explainability for Non-Linear Models
  • Explainability for Ensemble Models
  • Explainability for Time Series Models
  • Explainability for NLP
  • AI Model Fairness Using a What-If Scenario
  • Explainability for Deep Learning Models
  • Counterfactual Explanations for XAI Models
  • Contrastive Explanations for Machine Learning
  • Model-Agnostic Explanations by Identifying Prediction Invariance
  • Model Explainability for Rule-Based Expert Systems
  • Model Explainability for Computer Vision