Explainable Neural Networks based on Additive Index Models

By Joel Vaughan, Agus Sudjianto, Erind Brahimi, V. Nair. 2018

112 citations recorded by the source corpus.

Machine Learning algorithms are increasingly being used in recent years due to their flexibility in model fitting and increased predictive performance. However, the complexity of the models makes them hard for the data analyst to interpret the results and explain them without additional tools. This has led to much research in developing various approaches to understand the model behavior. In this paper, we present the Explainable Neural Network (xNN), a structured neural network designed especially to learn interpretable features. Unlike fully connected neural networks, the features engineered by the xNN can be extracted from the network in a relatively straightforward manner and the results displayed. With appropriate regularization, the xNN provides a parsimonious explanation of the relationship between the features and the output. We illustrate this interpretable feature--engineering property on simulated examples.

Subfields: Artificial Intelligence

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Citations in the corpus

Interpreting CNNs via Decision Trees (2018, background): “[32] distilled knowledge into an additive model for explanation.”
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies (2020, background): “in a neural network to represent an object [55], or to develop neural networks with the structure of an additive index model [56].”