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.┬áDespite 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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Evaluating the right AI Platform for your business

As businesses are increasingly laying their focus on artificial intelligence solutions, it might be important to understand what goes into implementing these solutions.

Most business product and solutions have now taken to the tone of customer satisfaction, making every solution look the same. It’s only a matter of time, that they will all offer or at least speak about artificial intelligence. I would assume, it would become increasingly difficult for the businesses to understand the truth behind these systems. Continue reading →