Abstract AI4VIS EthiCards are a card‐based approach designed to facilitate ethical exploration in the growing research of artificial intelligence (AI) for visualization (AI4VIS). The continuously increasing integration of AI and data visualization offers many benefits yet inevitably raises ethical concerns. AI4VIS applications, characterized by their interdisciplinary and multifaceted nature, necessitate that researchers, designers, and practitioners develop ethical awareness and critically reflect on their responsibilities within this domain. The AI4VIS EthiCards, organized into six types, i.e., Goal, AI4VIS Task, Technology, Ethical Principle, People‐In‐Focus , and Challenge , serve as a framework for discussion and ethical reflection. This card deck is designed for students, educators, and practitioners working with AI and visualization, and can be used as a flexible teaching framework, rapid reference, or self‐check tool in small‐group design and reflection activities. We refined the deck through five workshops and observed three main outcomes: participants' speculative yet feasible concepts revealed AI4VIS as a high‐potential design space; the deck supported more nuanced ethical awareness; and it functioned as a multi‐perspective scaffold and shared vocabulary that enabled people from different backgrounds to reason together across technological, user‐centred, and value‐oriented dimensions.

Technological advancements have resulted in great shifts in the production and consumption of news articles. This, in turn, lead to the requirement of new educational and practical frameworks. In this paper, we present a classification of data-driven news articles and related design patterns defined to describe their visual and textual components. Through the analysis of 162 data-driven news articles collected from news media, we identified five types of articles based on the level of data involvement and narrative complexity: Quick Update, Briefing, Chart Description, Investigation, and In-depth Investigation. We then identified 72 design patterns to understand and construct data-driven news articles. To evaluate this approach, we conducted workshops with 23 students from journalism, design, and sociology who were newly introduced to the subject. Our findings suggest that our approach can be used as an out-of-box framework for the formulation of plans and consideration of details in the workflow of data-driven news creation.