Product desciption
Responsible Ai In The Enterprise Practical Ai Risk Management For Explainable Auditable And Safe Models With Hyperscalers And Azure Openai 1st Edition Adnan Masood by Adnan Masood, Heather Dawe 9781803230528, 1803230525 instant download after payment.
Build and deploy your AI models successfully by exploring model governance, fairness, bias, and potential pitfalls
Key Features
Learn ethical AI principles, frameworks, and governance
Understand the concepts of fairness assessment and bias mitigation
Introduce explainable AI and transparency in your machine learning models
Book Description
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.
Who this book is for
This book is for data scientists, machine learning engineers, AI practitioners, IT professionals, business stakeholders, and AI ethicists who are responsible for implementing AI models in their organizations.
