The proliferation of applications using artificial intelligence (AI) systems has led to a growing number of users interacting with these systems through sophisticated interfaces. Human-computer interaction research has long shown that interfaces shape both user behavior and user perception of technical capabilities and risks. Yet, practitioners and researchers evaluating the social and ethical risks of AI systems tend to overlook the impact of anthropomorphic, deceptive, and immersive interfaces on human-AI interactions. Here, we argue that design features of interfaces with adaptive AI systems can have cascading impacts, driven by feedback loops, which extend beyond those previously considered. We first conduct a scoping review of AI interface designs and their negative impact to extract salient themes of potentially harmful design patterns in AI interfaces. Then, we propose Design-Enhanced Control of AI systems (DECAI), a conceptual model to structure and facilitate impact assessments of AI interface designs. DECAI draws on principles from control systems theory -- a theory for the analysis and design of dynamic physical systems -- to dissect the role of the interface in human-AI systems. Through two case studies on recommendation systems and conversational language model systems, we show how DECAI can be used to evaluate AI interface designs.

翻译：随着人工智能（AI）系统的广泛应用，用户通过复杂界面与这些系统进行交互的数量日益增长。人机交互研究早已表明，界面既会影响用户行为，也会影响用户对技术能力和风险的认知。然而，评估AI系统社会及伦理风险的实践者与研究者，往往忽略拟人化、欺骗性及沉浸式界面对人机交互的影响。本文认为，具有自适应AI系统的界面设计特征可通过反馈回路产生级联效应，其影响范围远超既有认知。我们首先通过范围综述梳理AI界面设计及其负面影响，提炼AI界面中潜在有害设计模式的核心主题。继而提出"AI系统的设计增强控制"（DECAI）概念模型，用以构建并促进AI界面设计的影响评估。DECAI借鉴控制系统理论——一种分析并设计动态物理系统的理论——来剖析界面在人机系统中的作用。通过推荐系统与对话语言模型系统两个案例研究，我们展示了如何运用DECAI评估AI界面设计。