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1
Introduction
2
Explainability
3
Challenges
4
Overview
5
Machine Learning Ops
6
Microsoft Tooling
7
Interpret ML
8
Fairness Toolkit Overview
9
Case Studies
10
Example
11
Adoption
12
Toolkits
13
H2O Toolkit
14
AI Open Scale
15
AI Friends 360
16
Whatif Tool
17
Performance Fairness
18
FactChecks
19
Amazon SageMaker
20
Questions
21
Lessons Learned
Description:
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.

Responsible AI in Industry - Lessons Learned in Practice

Association for Computing Machinery (ACM)
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