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Learning XAI: Explainable Artificial Intelligence

via LinkedIn Learning

Overview

Learn how explainable artificial intelligence (XAI) works and how it will impact data science-related projects and businesses.

Now that AI and machine learning are widespread, people are starting to ask, "Is the technology actually making the best decisions? Can AI be trusted? How and where do humans fit in?" Explainable artificial intelligence (XAI) is a solution that increases transparency about how AI systems make decisions and take actions. This course provides a solid introduction of how XAI works and the value it provides to data science-related businesses and initiatives from legal and commercial perspectives. Instructor Aki Ohashi, director of business development for PARC, a Xerox company, bridges the gap between AI's potential and pitfalls, presenting executives, entrepreneurs, managers, and team leaders with exactly what they need to know to stay on top of how AI affects their fields. He uses real-world examples and cases studies to show what XAI is, how it works, how it's being used right now, and where it may have the most impact in the future.

Syllabus

Introduction
  • Explainable AI: Expanding the frontiers of artificial intelligence
  • Introduction to AI and ML
1. Explainable AI (XAI)
  • What is XAI?
  • XAI techniques
  • The need for XAI: Business
  • The need for XAI: Legal
  • Limitations of XAI
2. Humans and Machines Have Different Strengths and Weaknesses
  • Humans are better at some things
  • Computers are better at some things
3. The Case for Human Machine Collaboration
  • Combining the strengths of humans and machines
  • Example: Centaur chess
4. Examples of Possible XAI Applications
  • Simple example: Driving directions
  • Medical example: Explainability for surgical health care and medicine
  • Business example 1: Marketing
  • Business example 2: Fraud detection
5. Next Steps
  • Full adoption will take time
  • Invest in XAI
Conclusion
  • Summary

Taught by

Aki Ohashi

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