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

The Responsible AI Toolbox

The Responsible AI Toolbox9 provides a range of tools and user interfaces to help developers and stakeholders of AI systems to better understand and monitor AI systems. The concept of responsible AI refers to a method of creating, evaluating, and using AI systems in a safe, ethical, and trustworthy way, making informed decisions, and taking responsible actions.

The toolbox includes four visualization widgets to analyze and make decisions about AI models:

  • The Responsible AI dashboard brings together various tools from the toolbox to provide a comprehensive view of responsible AI assessment and debugging. With this dashboard, you can identify model errors, understand why they happen, and take steps to address them. Additionally, the causal decision-making capabilities offer valuable insights to stakeholders and customers.
  • The Error Analysis dashboard helps identify model errors and identifies groups of data where a model performs poorly.
  • ...