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

Fairness metrics

Fairness metrics are mathematical measures to determine whether the model is making unbiased predictions and treating all groups fairly. Microsoft Fairlearn provides several fairness metrics, including statistical parity, equal opportunity, equalized odds, predictive parity, and demographic parity, measures critical in promoting fairness in AI systems and ensuring that all groups are treated equally by AI models. Let’s look at these metrics in more detail:

  • Demographic parity aims to ensure that the predictions made by a model are independent of membership to a sensitive group. In other words, demographic parity is achieved when the probability of a certain prediction is not dependent on sensitive group membership. In the binary classification scenario, demographic parity refers to equal selection rates across groups. For example, in the context of a resume-screening model, equal selection would mean that the proportion of applicants selected for a job...