Explainable AI, Blackboxes and Synthetic Data

In this 45 minutes podcast hosted by Ben Cole, Executive Editor at TechTarget, Vincent Granville, founder of MLTechniques.com and Executive ML Scientist, discusses the following topics:

  • Synthetic data design techniques, and how to identify business processes where most useful
  • How to test the quality of synthetic data
  • The benefits, and potential detriments, of explainable AI
  • Common modern enterprise data issues, such as managing unbalanced, inconsistent, small, outdated, and unstructured data
  • Ways to address data leakage, as well as small, wide and unobserved data
  • How synthetic data, explainable AI and other modern technologies are used to overcome these issues
  • Is a 1 trillion parameter neural network necessarily better than a much smaller one?

This podcast comes with two attachments (videos) that further illustrate the problems discussed:

  • Classification in action: An illustration of synthetic data and explainable AI
  • Video: Using rich synthetic data to test a curve fitting blackbox, and show how it works
The Business Analysis Benefits — and Limitations — of AI and Synthetic Data (45 mins)

In this Q&A, Vincent offers solutions to problems such as creating rich and meaningful synthetic data, assessing its quality, and how to use augmented data to enhance predictions and test/benchmark blackbox systems. An important question is whether explainable AI and blackboxes are incompatible, and how the two can happily be “married”. Another issue is what to do when blackboxes are deemed unethical and can not be used, for instance to automatically decide when an applicant should receive a loan or not, and no one can explain the reason why a blackbox led to a rejection. These issues and several others such as automating data cleaning, are addressed with a focus on solutions.

To access the podcast, follow this link.


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