  As automated machine learning (AutoML) systems continue to progress in both
sophistication and performance, it becomes important to understand the `how'
and `why' of human-computer interaction (HCI) within these frameworks, both
current and expected. Such a discussion is necessary for optimal system design,
leveraging advanced data-processing capabilities to support decision-making
involving humans, but it is also key to identifying the opportunities and risks
presented by ever-increasing levels of machine autonomy. Within this context,
we focus on the following questions: (i) How does HCI currently look like for
state-of-the-art AutoML algorithms, especially during the stages of
development, deployment, and maintenance? (ii) Do the expectations of HCI
within AutoML frameworks vary for different types of users and stakeholders?
(iii) How can HCI be managed so that AutoML solutions acquire human trust and
broad acceptance? (iv) As AutoML systems become more autonomous and capable of
learning from complex open-ended environments, will the fundamental nature of
HCI evolve? To consider these questions, we project existing literature in HCI
into the space of AutoML; this connection has, to date, largely been
unexplored. In so doing, we review topics including user-interface design,
human-bias mitigation, and trust in artificial intelligence (AI). Additionally,
to rigorously gauge the future of HCI, we contemplate how AutoML may manifest
in effectively open-ended environments. This discussion necessarily reviews
projected developmental pathways for AutoML, such as the incorporation of
reasoning, although the focus remains on how and why HCI may occur in such a
framework rather than on any implementational details. Ultimately, this review
serves to identify key research directions aimed at better facilitating the
roles and modes of human interactions with both current and future AutoML
systems.
