  Explainability and interpretability are two critical aspects of decision
support systems. Within computer vision, they are critical in certain tasks
related to human behavior analysis such as in health care applications. Despite
their importance, it is only recently that researchers are starting to explore
these aspects. This paper provides an introduction to explainability and
interpretability in the context of computer vision with an emphasis on looking
at people tasks. Specifically, we review and study those mechanisms in the
context of first impressions analysis. To the best of our knowledge, this is
the first effort in this direction. Additionally, we describe a challenge we
organized on explainability in first impressions analysis from video. We
analyze in detail the newly introduced data set, the evaluation protocol, and
summarize the results of the challenge. Finally, derived from our study, we
outline research opportunities that we foresee will be decisive in the near
future for the development of the explainable computer vision field.
