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Learning XAI: Explainable Artificial Intelligence (2019)

Learning XAI: Explainable Artificial Intelligence (2019)

1h 14mBeginner2019-02-22

Authors

Aki Ohashi

Aki Ohashi

Director of Business Development at PARC

Course details

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.

Topics include:
What is XAI?
XAI benefits and limitations
Humans vs. computers
XAI business examples
Investing in XAI

Skills covered

Artificial Intelligence FoundationsArtificial Intelligence (AI)One-Off

Concepts

0. Introduction

  • 01 - Explainable AI - Expanding the frontiers of artificial intelligence
  • 02 - Introduction to AI and ML

1. Explainable AI (XAI)

  • 03 - What is XAI
  • 04 - XAI techniques
  • 05 - The need for XAI - Business
  • 06 - The need for XAI - Legal
  • 07 - Limitations of XAI

2. Humans and Machines Have Different Strengths and Weaknesses

  • 08 - Humans are better at some things
  • 09 - Computers are better at some things

3. The Case for Human Machine Collaboration

  • 10 - Combining the strengths of humans and machines
  • 11 - Example - Centaur chess

4. Examples of Possible XAI Applications

  • 12 - Simple example - Driving directions
  • 13 - Medical example - Explainability for surgical health care and medicine
  • 14 - Business example 1 - Marketing
  • 15 - Business example 2 - Fraud detection

5. Next Steps

  • 16 - Full adoption will take time
  • 17 - Invest in XAI

Conclusion

  • 18 - Summary

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