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.

本文主要探讨在当前和未来的自动机器学习系统中，人与计算机交互如何发生（包括开发、部署和维护阶段的 HCI），不同类型的用户和利益相关方对 HCI 的期望是否存在差异，如何管理 HCI 以使自动机器学习获得人的信任和广泛接受，以及随着自动机器学习系统变得更加自主和学习能力增强，HCI 的基本特性是否会发生变化。研究关键领域包括自动化机器学习，人机交互，用户界面设计，人工智能信任等方面。