ModelSpeX: Model Specification Using Explainable Artificial Intelligence Methods
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Date
2020
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Explainable artificial intelligence (XAI) methods aim to reveal the non-transparent decision-making mechanisms of black-box models. The evaluation of insight generated by such XAI methods remains challenging as the applied techniques depend on many factors (e.g., parameters and human interpretation). We propose ModelSpeX, a visual analytics workflow to interactively extract human-centered rule-sets to generate model specifications from black-box models (e.g., neural networks). The workflow enables to reason about the underlying problem, to extract decision rule sets, and to evaluate the suitability of the model for a particular task. An exemplary usage scenario walks an analyst trough the steps of the workflow to show the applicability.
Description
@inproceedings{10.2312:mlvis.20201100,
booktitle = {Machine Learning Methods in Visualisation for Big Data},
editor = {Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko},
title = {{ModelSpeX: Model Specification Using Explainable Artificial Intelligence Methods}},
author = {Schlegel, Udo and Cakmak, Eren and Keim, Daniel A.},
year = {2020},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-113-7},
DOI = {10.2312/mlvis.20201100}
}