Design principles for artificial intelligence-augmented decision making: An action design research study
通过在一家服装电商公司开展行动设计研究，开发了一套人工智能增强决策系统，并提炼出可操作的设计原则，指导此类系统的设计与部署。
Artificial intelligence (AI) applications have proliferated, garnering significant interest among information systems (IS) scholars.AI-powered analytics, promising effective and low-cost decision augmentation, has become a ubiquitous aspect of contemporary organisations.Unlike traditional decision support systems (DSS) designed to support decisionmakers with fixed decision rules and models that often generate stable outcomes and rely on human agentic primacy, AI systems learn, adapt, and act autonomously, demanding recognition of IS agency within AI-augmented decision making (AIADM) systems.Given this fundamental shift in DSS; its influence on autonomy, responsibility, and accountability in decision making within organisations; the increasing regulatory and ethical concerns about AI use; and the corresponding risks of stochastic outputs, the extrapolation of prescriptive design knowledge from conventional DSS to AIADM is problematic.Hence, novel design principles incorporating contextual idiosyncrasies and practice-based domain knowledge are needed to overcome unprecedented challenges when adopting AIADM.To this end, we conduct an action design research (ADR) study within an e-commerce company specialising in producing and selling clothing.We develop an AIADM system to support marketing, consumer engagement, and product design decisions.Our work contributes to theory and practice with a set of actionable design principles to guide AIADM system design and deployment.