Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience
Despite the widespread use of artificial intelligence (AI), designing user experiences (UX) for AI-powered systems remains challenging. UX designers face hurdles understanding AI technologies, such as pre-trained language models, as design materials. This limits their ability to ideate and make decisions about whether, where, and how to use AI. To address this problem, we bridge the literature on AI design and AI transparency to explore whether and how frameworks for transparent model reporting can support design ideation with pre-trained models. By interviewing 23 UX practitioners, we find that practitioners frequently work with pre-trained models, but lack support for UX-led ideation. Through a scenario-based design task, we identify common goals that designers seek model understanding for and pinpoint their model transparency information needs. Our study highlights the pivotal role that UX designers can play in Responsible AI and calls for supporting their understanding of AI limitations through model transparency and interrogation.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Understanding U.S. Users' Security and Privacy Transparency Needs for Consumer-Facing Generative AI
Users increasingly rely on consumer-facing generative AI (GenAI) for tasks ranging from everyday needs to sensitive use cases. Yet, it remains unclear whether and how existing security and privacy (S&P) communications in…
Positioning Modular Co-Design in Future HRI Design Research
Design-oriented HRI is increasingly interested in robots as long-term companions, yet many designs still assume a fixed form and a stable set of functions. We present an ongoing design research program that treats modula…
AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap
The rise of powerful large language models (LLMs) brings about tremendous opportunities for innovation but also looming risks for individuals and society at large. We have reached a pivotal moment for ensuring that LLMs …
Understanding the Information needs of Social Scientists in Germany
The information needs of social science researchers are manifold and almost studied in every decade since the 1950s. With this paper, we contribute to this series and present the results of three studies. We asked 367 so…
From Sea to System: Exploring User-Centered Explainable AI for Maritime Decision Support
As autonomous technologies increasingly shape maritime operations, understanding why an AI system makes a decision becomes as crucial as what it decides. In complex and dynamic maritime environments, trust in AI depends …