Responsible AI in Industry - Lessons Learned in Practice

Explore a comprehensive tutorial on responsible AI implementation in industry settings, featuring insights from experts at Amazon, Google, and Microsoft. Delve into key topics such as explainability, machine learning operations, and fairness toolkits. Learn about real-world case studies, practical challenges, and lessons learned from adopting responsible AI practices. Gain valuable knowledge on various AI toolkits, including Microsoft's Interpret ML and Fairness Toolkit, H2O Toolkit, AI Open Scale, and Amazon SageMaker. Discover strategies for performance fairness, fact-checking, and addressing ethical considerations in AI development and deployment.

Introduction
Explainability
Challenges
Overview
Machine Learning Ops
Microsoft Tooling
Interpret ML
Fairness Toolkit Overview
Case Studies
Example
Adoption
Toolkits
H2O Toolkit
AI Open Scale
AI Friends 360
Whatif Tool
Performance Fairness
FactChecks
Amazon SageMaker
Questions
Lessons Learned