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


Data and model drift refer to a phenomenon that occurs when the statistical properties of a dataset or underlying model change over time. In this chapter, we reviewed how this can have an adverse impact on the predictions of models and, hence, on business outcomes. To make sure models function as desired, companies implement an ML life cycle that ensures design, development, deployment, and monitoring best practices are in place. Drifts can happen for a variety of reasons, including changes in the underlying population and changes in the way data is collected. When data drift happens, it can create bias in ML models that are trained on this data, which can be quite problematic for regulations and compliance.

In this chapter, we reviewed several ways to detect and mitigate bias due to data or model drift, and to monitor your training and validation error rates closely using different tools, including open source and commercial hyperscaler products. There are various other...