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

Foundation models

A foundation model1 is an AI model that is trained on a large amount of unlabeled data using self-supervised learning techniques, resulting in a highly adaptable model that can be fine-tuned to a variety of downstream tasks. Since their introduction in 2018, foundation models have revolutionized the field of AI and opened up new possibilities for natural language processing (NLP), image recognition, and other applications. Early examples of foundation models were large pre-trained language models, such as BERT and GPT-3, which demonstrated the power of unsupervised learning and transfer learning.

Domain-specific models based on different kinds of tokens have also been developed, including medical codes. Multimodal foundation models such as DALL-E, Flamingo, Florence, and NOOR have also been produced. The term “foundation model”2 was popularized by Stanford University’s Human-Centered Artificial Intelligence’s (HAI) Center for Research...