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

References and further reading

  1. The term foundation models lacks a widely agreed-upon definition. While some argue that these models must be large, trained using unsupervised or self-supervised learning, and serve as a basis for further fine-tuning, others disagree and suggest that the term is unnecessarily grandiose. Many experts outside of Stanford have pushed back against the term, citing concerns that it may be an attempt to coin a new term for something that does not need one. Instead, it may be more effective to use clearer, more descriptive language such as large pre-trained models or Large Self-Supervised Models (LSSMs), which more accurately capture the essence of these models without overemphasizing their importance.
  2. On the Opportunities and Risks of Foundation Models:
  3. On the Opportunities and Risks of Foundation Models:
  4. OpenAI Technical Report of GPT-4: