Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience
This addresses the problem of enabling UX designers to effectively ideate and make decisions for AI-powered systems, though it is incremental as it builds on existing AI transparency frameworks.
The study tackled the challenge UX designers face in understanding AI technologies like pre-trained language models for design ideation, finding that practitioners lack support for UX-led ideation and identifying their specific model transparency information needs through interviews with 23 UX practitioners and a scenario-based design task.
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.