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Human-Understandable Explanations of Neural Networks

The 21st century is characterized by a flood of data. To deal with this amount of data, research on neural networks has gained significant momentum over the past few years. Due to the huge success in pattern recognition, they have become a powerful tool for classification and forecasting in statistics, computer science, and economics. Despite their application in many disciplines, neural networks are black box models. They do not give any insights into the structure of the approximated function. Recent research has attempted to explain these black-boxes. However, the focus so far has been to explain decisions of a neural network in a technical way for computer science experts. As neural networks get more common, it is crucial to develop approaches that allow for explanations of neural networks understandable to non-experts. This means that a human can understand the cause of even a simple decision made by the neural network and can consistently interpret the model's result. For this reason, this work provides a framework to gain human-understandable explanations for neural networks by considering specific goals of explanations. These are transparency, scrutability, trust, effectiveness, persuasiveness, efficiency, and satisfaction.