Maximilian Becker
Adv. Artif. Intell. Mach. Learn., 1 (2):94-114
Maximilian Becker : Vision and Fusion Lab, Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology (KIT), Germany.
DOI: 10.54364/AAIML.2021.1107
Article History: Received on: 05-Jul-21, Accepted on: 01-Aug-21, Published on: 11-Aug-21
Corresponding Author: Maximilian Becker
Email: MAXIMILIAN.BECKER@KIT.EDU
Citation: Maximilian Becker, Nadia Burkart, Pascal Birnstill, Jürgen Beyerer (2021). A Step Towards Global Counterfactual Explanations: Approximating the Feature Space Through Hierarchical Division and Graph Search. Adv. Artif. Intell. Mach. Learn., 1 (2 ):94-114
The field of Explainable Artificial Intelligence (XAI) tries to make learned models more understandable. One type of
explanation for such models are counterfactual explanations. Counterfactual explanations explain the decision for a specific
instance, the factual, by providing a similar instance which leads to a different decision, the counterfactual. In this work a new
approaches around the idea of counterfactuals was developed. It generates a data structure over the feature space of a
classification problem to accelerate the search for counterfactuals and augments them with global explanations. The approach
maps the feature space by hierarchically dividing it into regions which belong to the same class. It is applicable in any case
where predictions can be generated for input data, even without direct access to the model. The framework works well for
lower-dimensional problems but becomes unpractical due to high computation times at around 12 to 15 dimensions.