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.

设计增强的 AI 系统控制 (DECAI) 是一个概念性模型，它着眼于探讨接口在人工智能系统中的作用，通过两个案例研究展示了 DECAI 的应用价值。