Book Image

Hands-On Explainable AI (XAI) with Python

By : Denis Rothman
Book Image

Hands-On Explainable AI (XAI) with Python

By: Denis Rothman

Overview of this book

Effectively translating AI insights to business stakeholders requires careful planning, design, and visualization choices. Describing the problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex. Hands-On Explainable AI (XAI) with Python will see you work with specific hands-on machine learning Python projects that are strategically arranged to enhance your grasp on AI results analysis. You will be building models, interpreting results with visualizations, and integrating XAI reporting tools and different applications. You will build XAI solutions in Python, TensorFlow 2, Google Cloud’s XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source XAI tools for Python that can be used throughout the machine learning project life cycle. You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting the visualization of machine learning models into user explainable interfaces. By the end of this AI book, you will possess an in-depth understanding of the core concepts of XAI.
Table of Contents (16 chapters)
14
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15
Index

Chapter 5, Building an Explainable AI Solution from Scratch

  1. Moral considerations mean nothing in AI as long as it's legal. (True|False)

    False. You have conflicts with people that are offended by AI solutions that do not take moral considerations into account.

  2. Explaining AI with an ethical approach will help users trust AI. (True|False)

    True. An ethical approach will make your AI programs and explanations trustworthy.

  3. There is no need to check whether a dataset contains legal data. (True|False)

    False. You must verify if the data used is legal or not.

  4. Using machine learning algorithms to verify datasets is productive. (True|False)

    True. ML can provide automated help to check datasets.

  5. Facets Dive requires an ML algorithm. (True|False)

    False. You can load and analyze raw data without running an ML algorithm first.

  6. You can anticipate ML outputs with Facets Dive...