  With the growing complexity of deep learning methods adopted in practical
applications, there is an increasing and stringent need to explain and
interpret the decisions of such methods. In this work, we focus on explainable
AI and propose a novel generic and model-agnostic framework for synthesizing
input exemplars that maximize a desired response from a machine learning model.
To this end, we use a generative model, which acts as a prior for generating
data, and traverse its latent space using a novel evolutionary strategy with
momentum updates. Our framework is generic because (i) it can employ any
underlying generator, e.g. Variational Auto-Encoders (VAEs) or Generative
Adversarial Networks (GANs), and (ii) it can be applied to any input data, e.g.
images, text samples or tabular data. Since we use a zero-order optimization
method, our framework is model-agnostic, in the sense that the machine learning
model that we aim to explain is a black-box. We stress out that our novel
framework does not require access or knowledge of the internal structure or the
training data of the black-box model. We conduct experiments with two
generative models, VAEs and GANs, and synthesize exemplars for various data
formats, image, text and tabular, demonstrating that our framework is generic.
We also employ our prototype synthetization framework on various black-box
models, for which we only know the input and the output formats, showing that
it is model-agnostic. Moreover, we compare our framework (available at
https://github.com/antoniobarbalau/exemplar) with a model-dependent approach
based on gradient descent, proving that our framework obtains equally-good
exemplars in a shorter computational time.
