This paper aims to develop a semi-formal design space for Human-AI
interactions, by building a set of interaction primitives which specify the
communication between users and AI systems during their interaction. We show
how these primitives can be combined into a set of interaction patterns which
can provide an abstract specification for exchanging messages between humans
and AI/ML models to carry out purposeful interactions. The motivation behind
this is twofold: firstly, to provide a compact generalisation of existing
practices, that highlights the similarities and differences between systems in
terms of their interaction behaviours; and secondly, to support the creation of
new systems, in particular by opening the space of possibilities for
interactions with models. We present a short literature review on frameworks,
guidelines and taxonomies related to the design and implementation of HAI
interactions, including human-in-the-loop, explainable AI, as well as hybrid
intelligence and collaborative learning approaches. From the literature review,
we define a vocabulary for describing information exchanges in terms of
providing and requesting particular model-specific data types. Based on this
vocabulary, a message passing model for interactions between humans and models
is presented, which we demonstrate can account for existing systems and
approaches. Finally, we build this into design patterns as mid-level constructs
that capture common interactional structures. We discuss how this approach can
be used towards a design space for Human-AI interactions that creates new
possibilities for designs as well as keeping track of implementation issues and
concerns.

该研究旨在开发半正式的设计空间，为人工智能交互建立一套规范用户与 AI 系统在交互过程中的通信的交互基元，进而将这些基元组合为一组交互模式，为人类和 AI/ML 模型之间交换信息提供抽象规范，以评估现有实践的相似性和差异性，并为新系统的创建提供可能性空间。