It is said that beauty is in the eye of the beholder. But how exactly can we
characterize such discrepancies in interpretation? For example, are there any
specific features of an image that makes person A regard an image as beautiful
while person B finds the same image displeasing? Such questions ultimately aim
at explaining our individual ways of interpretation, an intention that has been
of fundamental importance to the social sciences from the beginning. More
recently, advances in computer science brought up two related questions: First,
can computational tools be adopted for analyzing ways of interpretation?
Second, what if the "beholder" is a computer model, i.e., how can we explain a
computer model's point of view? Numerous efforts have been made regarding both
of these points, while many existing approaches focus on particular aspects and
are still rather separate. With this paper, in order to connect these
approaches we introduce a theoretical framework for analyzing interpretation,
which is applicable to interpretation of both human beings and computer models.
We give an overview of relevant computational approaches from various fields,
and discuss the most common and promising application areas. The focus of this
paper lies on interpretation of text and image data, while many of the
presented approaches are applicable to other types of data as well.Comment: Preprint submitted to Digital Signal Processin