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)
Part 1: Bigot in the Machine – A Primer
Part 2: Enterprise Risk Observability Model Governance
Part 3: Explainable AI in Action


Phew! That was a lot of stuff! Let’s do a quick recap.

This chapter began with an examination of foundation models, exploring how they work and the potential for bias in these models. It then delved into the enterprise use of foundation models, with a particular focus on bias remediation. Biases in GPT-3 and the limitations of large language models were also explored. We then provided an overview of OpenAI and Azure OpenAI, with a detailed look at the Azure OpenAI platform. Azure OpenAI was examined in depth, with information provided on accessing the API, the Code of Conduct, content filtering, use cases, governance, and potential risks. The chapter also covered data privacy and security for Azure OpenAI Service, as well as AI governance for enterprise use.

This chapter also provided a guide to getting started with Azure OpenAI and consuming the GPT-3 model using an API. It concluded with an overview of Azure OpenAI Service models, including the code generation...