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

Data, privacy, and security for Azure OpenAI Service

One of the most asked questions about Azure OpenAI (or any API service for that matter) is about data retention and customer controls.

Customer training data and fine-tuned OpenAI models are stored in Azure Storage, encrypted at rest with Microsoft-managed keys, and logically isolated with their Azure subscription and API credentials.

Customers can delete uploaded files and trained models via the DELETE API operation. Text prompts, queries, and responses may be temporarily stored for up to 30 days, encrypted, and are only accessible to authorized engineers for debugging, investigating misuse, or improving content filtering.

AI governance for the enterprise use of Azure OpenAI

Enterprise governance in Azure OpenAI involves ensuring that the service is used in a way that aligns with an organization’s overall goals and values, ensuring that the benefits of AI are maximized while minimizing potential harm. This includes...