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.

本文介绍了解释性和可解释性对于决策支持系统的两个关键方面，并探索了这些方面在计算机视觉中的应用，特别是在人类行为分析方面。文章描述了一个挑战，旨在提升解释性在第一印象分析中的应用。通过对引入的数据集，评估协议的详细分析和解释性挑战的结果，总结研究机会，并确定为解释性计算机视觉领域的发展制定规划。