TransforM – Transparency for Machinery in Personal Pervasive Smart Spaces

TransforM项目是SPP-2199项目PerforM的后续项目，该项目研究了作为机器人智能空间交互范例的个性。PerforM已经建立了一个所谓的“房间智能”，即，智能生活环境与机器人元素和智能厨房电器，它可以作为一个连贯的实体，并表现出一定的个性对它的用户。在用户研究中探索这一范式的过程中，很明显，在这种环境的透明性（用户可以理解幕后发生的事情）和技术的不可见性（特别是在家庭环境中，用户希望事情简单地工作，而不是在舒适的氛围中处理所有细节）之间存在冲突。因此，提供适当的透明度对于接受这种环境至关重要。基于可解释人工智能和人际沟通的透明度概念，我们研究了如何在个人智能空间（如房间智能及其他）中构建和传达透明度。我们建议设计一种语言和工具箱的透明度积木，我们将评估它在几个用户研究。除其他外，这些研究将帮助我们了解用户如何感知透明度的构建模块，透明度如何与信任和接受有关，以及是否有可能确定一个“透明度最佳点”。此外，我们将探索适应性透明度的潜力，它考虑到个人用户的特征和偏好（例如，技术专长、个性差异）。基于这些研究，我们将为未来的智能空间提取一般的可扩展交互范例和设计指南，确保这些既不太不透明也不太压倒性，并且通过适当的透明度水平，将获得足够的用户信任。

The project TransforM is a follow-up project to the SPP-2199 project PerforM, which investigated personality as an interaction paradigm for robotic smart spaces. PerforM has built a so-called "room intelligence", i.e., smart living environment with robotic elements and intelligent kitchen appliances, which can be addressed as a coherent entity and exhibits a certain personality towards its users. During the exploration of this paradigm in user studies, it became clear that there is a conflict between the wish for transparency of such an environment (the user can understand what is going on behind the scenes) and the wish for invisibility of technology, especially in the home context (the user wants things to simply work, and not deal with all the details, in a cozy atmosphere). Providing the right level of transparency turns therefore out to be crucial for acceptance of such an environment. Building on transparency concepts from Explainable AI and interpersonal communication, we investigate how transparency can be constructed and communicated in personal smart spaces such as the room intelligence and beyond. We propose to design a language and toolbox of transparency building blocks which we will evaluate it in several user studies. Among other, these studies will help us to understand how the transparency building blocks are perceived by the user, how transparency relates to trust and acceptance and whether it is possible to identify a “transparency sweet spot”. Moreover, we will explore the potential of adaptable transparency which considers individual user characteristics and preferences (e.g., technical expertise, personality differences). Based on these studies, we will extract general scalable interaction paradigms and design guidelines for future smart spaces, which ensure that these are neither too intransparent nor too overwhelming, and will – by the right level of transparency – receive an adequate calibrated amount of trust from their users.