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

Azure Machine Learning model interpretability

Microsoft Azure Machine Learning is a cloud-based service that provides data scientists with the ability to build, train, and deploy ML models. Azure Machine Learning makes it easy to track experiments, manage datasets, and collaborate with other data scientists. With this cloud ML platform, Azure Machine Learning offers tools for building and managing bespoke ML projects using managed Jupyter notebooks, Azure Machine Learning Designer (for drag-and-drop ML pipelines), and an automated ML UI. Microsoft also offers Azure Cognitive Services, which developers can use with no specific ML knowledge to use prebuilt AI as a service for use cases in speech, language, vision, and decision-making. Developers and data scientists can also use Azure’s OpenAI Service, which is in preview at the time of writing, to experiment with the most sophisticated language model technologies in the world.

Microsoft’s responsible AI ecosystem can...