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

Model and data security

Note the following peculiarities for model and data security:

  • Authentication: Authentication is the process of verifying a user’s identity to ensure that only authorized individuals can access high-stakes ML systems. Examples of authentication methods include login credentials, multi-factor authentication, and biometrics. By implementing strong authentication mechanisms, organizations can prevent unauthorized access and reduce the risk of malicious activities.
  • Interpretable, fair, or private models: Interpretable models are designed to be more transparent and easier to understand, making them simpler to debug and secure. Fair models aim to minimize bias and ensure equitable treatment for all users, reducing potential legal and reputational risks. Private models protect sensitive data, often using privacy-preserving techniques such as differential privacy. By prioritizing accuracy, interpretability, fairness, and privacy in modeling techniques...