Pathways for Design Research on Artificial Intelligence

• 成果类型: Article
• 署名作者: Abbasi, Ahmed; Parsons, Jeffrey; Pant, Gautam; Sheng, Olivia R. Liu; Sarker, Suprateek
• 署名单位: University of Notre Dame; Memorial University Newfoundland; University of Illinois System; University of Illinois Urbana-Champaign; Arizona State University; Arizona State University-Tempe; University of Virginia
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2024.editorial.v35.n2
• 发表日期: 2024
• 页码: 441-459
• 关键词: science research; big data; systems; FRAMEWORK; analytics; QUALITY; REPRESENTATION; discipline; retrieval; KNOWLEDGE

摘要

An expanding body of information systems research is adopting a design perspective on artificial intelligence (AI), wherein researchers prescribe solutions to problems using AI approaches rather than describing or explaining AI -related phenomena being studied. In this editorial, we address some of the challenges faced in publishing design research related to AI and articulate viable pathways for publishing such work. More specifically, we highlight six major impediments, use the explosion in the state of the art for large language models to underscore these impediments, propose some pathways for overcoming the impediments, and use several example articles to illustrate how the pathways can be followed for different types of AI -related design artifacts.