Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience

Autor:	Q. Vera Liao, Hariharan Subramonyam, Jennifer Wang, Jennifer Wortman Vaughan
Rok vydání:	2023
Předmět:	FOS: Computer and information sciences Artificial Intelligence (cs.AI) Computer Science - Artificial Intelligence Computer Science - Human-Computer Interaction Human-Computer Interaction (cs.HC)
DOI:	10.48550/arxiv.2302.10395
Popis:	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. Comment: Accepted at ACM CHI Conference on Human Factors in Computing Systems (CHI 2023)
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::427e360193db2e6a5aac7f48f7c2002a Zobrazit plný text záznamu