Explainable AI: Visualizing and Interpreting Machine Learning

Machine learning algorithms operate in a so-called black box — so the inputs and outputs are known, but how or why an algorithm makes the recommendation it does is not clear.¬†Despite the AI media frenzy, companies and governments are concerned about the machine learning black box. The EU’s General Data Protection Regulation, which takes effect next year, includes a right to explanation clause, or an explanation of how a model made a decision.

The Explainable AI (XAI) program aims to create a suite of machine learning techniques that:

  • Produce more explainable models, while maintaining a high level of learning performance (prediction accuracy); and
  • Enable human users to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners.

New machine-learning systems will have the ability to explain their rationale, characterize their strengths and weaknesses, and convey an understanding of how they will behave in the future. The strategy for achieving that goal is to develop new or modified machine-learning techniques that will produce more explainable models.

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Prism of Possibilities

Plumb5 integrated learning engine can provide businesses with a range of growth possibilities influencing top-line and bottom-line revenue growth. The AI engine which learns from the unified customer stack using segment-of-one customer data is able to address individual patterns and deliver personalization based on individual patterns. For a more global view, these similar individual patterns are grouped for users to view at a macro-segment level.

Listed are the aspects that are solved or optimized in order to ensure business profitability through AI and Automation. Continue reading →