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 OpenAI Service models

Azure OpenAI offers a variety of models that are categorized into families based on their intended function. The available model families are listed in a table on the platform, though not all models are accessible in every region. Each model family consists of multiple models with different capabilities, which are denoted by their names. The order of these names alphabetically indicates the relative capabilities and costs of each model within a given family. For instance, the GPT-3 model family includes models such as Ada, Babbage, Curie, and Davinci, which are arranged in increasing order of capability and cost, with Davinci being the most advanced and expensive model.

It is important to note that even though most people have only heard of ChatGPT, or GPT-3, there are various notable generative AI models, including LLM and image generation models. It’s worth noting that the number of parameters is not the only metric to evaluate a model’...