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

The environmental impact

The environmental impact of AI is manifold – not only that deep learning exacerbates energy use by training the models but also its impact on oil and gas discovery. In their study, researchers at the University of Massachusetts at Amherst47 estimated that training a large deep learning model produces 626,000 pounds of planet-warming carbon dioxide, equal to the lifetime emissions of 5 cars48.

As we consider the negative impacts of AI on climate, particularly in relation to GPU usage for LLMs and electricity consumption, we recognize that the extensive energy required for training deep learning models contributes to a significant carbon footprint. Additionally, inefficiencies in hardware and algorithms, coupled with the increasing demand for AI applications, exacerbate the environmental impact due to the growing reliance on energy-consuming data centers. Rapid advancements in AI-driven technologies lead to a rise in electronic waste, causing environmental...