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 ML life cycle

Reproducibility is crucial in ML to ensure consistent and replicable results. Integrating the MLOps life cycle promotes reproducibility, auditability, ethics, reliability, and trustworthiness in ML models. Auditability ensures the model behaves as expected and provides transparency of its workings and data usage. Ethical guidelines addressing privacy rights, data accuracy, and transparency are essential when deploying AI systems. A well-defined ML life cycle facilitates adherence to these guidelines. Model monitoring guarantees reliability by operationalizing the trustworthiness of ML models, particularly in high-risk applications and regulated industries. Implementing routine testing, validation by outside experts, and ongoing performance monitoring is essential. Clear explanations for decision-making and user control over their own data ensure a transparent, repeatable, and auditable AI system.

Adopting an ML life cycle

ML operationalization is the process...