  Counterfactual Explanations (CEs) are an important tool in Algorithmic
Recourse for addressing two questions: 1. What are the crucial factors that led
to an automated prediction/decision? 2. How can these factors be changed to
achieve a more favorable outcome from a user's perspective? Thus, guiding the
user's interaction with AI systems by proposing easy-to-understand explanations
and easy-to-attain feasible changes is essential for the trustworthy adoption
and long-term acceptance of AI systems. In the literature, various methods have
been proposed to generate CEs, and different quality measures have been
suggested to evaluate these methods. However, the generation of CEs is usually
computationally expensive, and the resulting suggestions are unrealistic and
thus non-actionable. In this paper, we introduce a new method to generate CEs
for a pre-trained binary classifier by first shaping the latent space of an
autoencoder to be a mixture of Gaussian distributions. CEs are then generated
in latent space by linear interpolation between the query sample and the
centroid of the target class. We show that our method maintains the
characteristics of the input sample during the counterfactual search. In
various experiments, we show that the proposed method is competitive based on
different quality measures on image and tabular datasets -- efficiently returns
results that are closer to the original data manifold compared to three
state-of-the-art methods, which are essential for realistic high-dimensional
machine learning applications.
