Unlock the Black Box: The Importance of Explainability in Machine Learning

Author(s): Janik and Patrick Tinz

Originally published on Towards AI.

An Overview of the Principles of Explainability

Photo by Headway on Unsplash

Machine learning (ML) has great potential for improving processes and products. A challenge is to explain the predictions of the ML algorithms. Trust and transparency are central arguments for the explainability of decision findings by an ML model.

In this article, we introduce the basic concepts of Explainable Artificial Intelligence (XAI). Furthermore, we present the properties or requirements of an explanation. In the context of this article, we use the terms interpretable and explainable synonymously.

The greater the interpretability of an ML model, the easier it is for a human to understand a particular decision or prediction. One… Read the full blog for free on Medium.

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Published via Towards AI