SPARTA Publications

All accepted publications from SPARTA partners under its funding.

ModelSpeX: Model Specification Using Explainable Artificial Intelligence Methods

Schlegel, Udo; Cakmak, Eren; Keim, Daniel A.


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.

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