Efforts in levering Artificial Intelligence (AI) in decision support systems
have disproportionately focused on technological advancements, often
overlooking the alignment between algorithmic outputs and human expectations.
To address this, explainable AI promotes AI development from a more
human-centered perspective. Determining what information AI should provide to
aid humans is vital, however, how the information is presented, e. g., the
sequence of recommendations and the solicitation of interpretations, is equally
crucial. This motivates the need to more precisely study Human-AI interaction
as a pivotal component of AI-based decision support. While several empirical
studies have evaluated Human-AI interactions in multiple application domains in
which interactions can take many forms, there is not yet a common vocabulary to
describe human-AI interaction protocols. To address this gap, we describe the
results of a systematic review of the AI-assisted decision making literature,
analyzing 105 selected articles, which grounds the introduction of a taxonomy
of interaction patterns that delineate various modes of human-AI interactivity.
We find that current interactions are dominated by simplistic collaboration
paradigms and report comparatively little support for truly interactive
functionality. Our taxonomy serves as a valuable tool to understand how
interactivity with AI is currently supported in decision-making contexts and
foster deliberate choices of interaction designs.

人工智能在决策支持系统中的应用主要集中在技术进步方面，忽视了算法输出与人类期望的一致性。为了解决这个问题，可解释的人工智能提倡更加以人为中心的发展。为了更好地理解人与人工智能的交互，我们通过对 105 篇选定文章进行系统综述，提出了一种交互模式分类系统，以深入研究不同人机交互方式。通过对现有研究的分析发现，现有的交互方式主要是简单的合作模式，对于真正的交互功能的支持相对较少。我们的分类系统将有助于理解当前决策环境中人与人工智能的交互方式，并促进交互设计的有意选择。