  "Human-aware" has become a popular keyword used to describe a particular
class of AI systems that are designed to work and interact with humans. While
there exists a surprising level of consistency among the works that use the
label human-aware, the term itself mostly remains poorly understood. In this
work, we retroactively try to provide an account of what constitutes a
human-aware AI system. We see that human-aware AI is a design oriented
paradigm, one that focuses on the need for modeling the humans it may interact
with. Additionally, we see that this paradigm offers us intuitive dimensions to
understand and categorize the kinds of interactions these systems might have
with humans. We show the pedagogical value of these dimensions by using them as
a tool to understand and review the current landscape of work related to
human-AI systems that purport some form of human modeling. To fit the scope of
a workshop paper, we specifically narrowed our review to papers that deal with
sequential decision-making and were published in a major AI conference in the
last three years. Our analysis helps identify the space of potential research
problems that are currently being overlooked. We perform additional analysis on
the degree to which these works make explicit reference to results from social
science and whether they actually perform user-studies to validate their
systems. We also provide an accounting of the various AI methods used by these
works.
