Book Image

Responsible AI in the Enterprise

By : Adnan Masood, Heather Dawe
5 (1)
Book Image

Responsible AI in the Enterprise

5 (1)
By: Adnan Masood, Heather Dawe

Overview of this book

Responsible AI in the Enterprise is a comprehensive guide to implementing ethical, transparent, and compliant AI systems in an organization. With a focus on understanding key concepts of machine learning models, this book equips you with techniques and algorithms to tackle complex issues such as bias, fairness, and model governance. Throughout the book, you’ll gain an understanding of FairLearn and InterpretML, along with Google What-If Tool, ML Fairness Gym, IBM AI 360 Fairness tool, and Aequitas. You’ll uncover various aspects of responsible AI, including model interpretability, monitoring and management of model drift, and compliance recommendations. You’ll gain practical insights into using AI governance tools to ensure fairness, bias mitigation, explainability, privacy compliance, and privacy in an enterprise setting. Additionally, you’ll explore interpretability toolkits and fairness measures offered by major cloud AI providers like IBM, Amazon, Google, and Microsoft, while discovering how to use FairLearn for fairness assessment and bias mitigation. You’ll also learn to build explainable models using global and local feature summary, local surrogate model, Shapley values, anchors, and counterfactual explanations. By the end of this book, you’ll be well-equipped with tools and techniques to create transparent and accountable machine learning models.
Table of Contents (16 chapters)
1
Part 1: Bigot in the Machine – A Primer
4
Part 2: Enterprise Risk Observability Model Governance
9
Part 3: Explainable AI in Action

Other Books You May Enjoy

If you enjoyed this book, you may be interested in these other books by Packt:

Natural Language Understanding with Python

Deborah A. Dahl

ISBN: 978-1-80461-342-9

  • Explore the uses and applications of different NLP techniques
  • Understand practical data acquisition and system evaluation workflows
  • Build cutting-edge and practical NLP applications to solve problems
  • Master NLP development from selecting an application to deployment
  • Optimize NLP application maintenance after deployment
  • Build a strong foundation in neural networks and deep learning for NLU

Platform and Model Design for Responsible AI

Amita Kapoor, Sharmistha Chatterjee

ISBN: 978-1-80323-707-7

  • Understand the threats and risks involved in ML models
  • Discover varying levels of risk mitigation strategies and risk tiering tools
  • Apply traditional and deep learning optimization...