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.
Understand explainable AI fundamentals, underlying methods, and techniques
Explore model governance, including building explainable, auditable, and interpretable machine learning models
Use partial dependence plot, global feature summary, individual condition expectation, and feature interaction
Build explainable models with global and local feature summary, and influence functions in practice
Design and build explainable machine learning pipelines with transparency
Discover Microsoft FairLearn and marketplace for different open-source explainable AI tools and cloud platforms