  The NASA Planetary Data System (PDS) hosts millions of images of planets,
moons, and other bodies collected throughout many missions. The ever-expanding
nature of data and user engagement demands an interpretable content
classification system to support scientific discovery and individual curiosity.
In this paper, we leverage a prototype-based architecture to enable users to
understand and validate the evidence used by a classifier trained on images
from the Mars Science Laboratory (MSL) Curiosity rover mission. In addition to
providing explanations, we investigate the diversity and correctness of
evidence used by the content-based classifier. The work presented in this paper
will be deployed on the PDS Image Atlas, replacing its non-interpretable
counterpart.
