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

References and further reading

  1. The seminal paper The Unreasonable Effectiveness of Data, by Alon Halevy, Peter Norvig, and Fernando Pereira, argues that the availability of large datasets can lead to highly accurate predictions or models, even with relatively simple algorithms. The authors give examples from various fields to demonstrate the power of data in driving effective solutions. They suggest that future AI systems will likely depend heavily on large amounts of data and that data will continue to play a crucial role in shaping the future of AI and other fields.
  2. https://news.mit.edu/2022/artificial-intelligence-predicts-patients-race-from-medical-images-0520
  3. The phenomenon of proxy or highly correlated features in ML is commonly referred to as “feature leakage," which can lead to biased or unfair predictions. These highly correlated features are sometimes referred to as confounding variables, surrogate features, hidden variables, indicator features, substitute...