A Modern and Scalable Approach to Responsible AI

Audit, Assess, and Evaluate AI Systems

Understanding machine learning (ML) systems is a critical task for data scientists and non-technical profiles alike as organizations aim to integrate AI applications on an enterprise-wide level.

In this ebook, we explore practices to identify cutting-edge and responsible strategies for managing high-impact AI systems and work to understand the concepts and techniques of model interpretability and explainability.

2021 OReilly Responsible AI Mini 3D Cover

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Understanding the Task at Hand

Early Release Chapters 1-7 Feature:

  • A precis of all of that model governance encompasses today and the best execution methods for practitioners
  • Insight into the best practices for debugging ML systems for safety and performance
  • An overview of security for ML to effectively audit for any potential vulnerabilities
  • An introduction to interpretable and explainable ML, with a discussion of applying interpretable models and post-hoc explanations
  • Strategies for managing bias in ML, including testing for bias, technical factors in mitigating bias, and more