Design and Appropriation of Knowledge and AI Systems
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ItemNo Ground Truth at Sea – Developing High-Accuracy AI Decision-Support for Complex Environments( 2023-01-03)As AI decision-support systems are increasingly developed for applications outside of traditional organizational confinements, developers are confronted with new sources of complexity they need to address. However, we know little about how AI applications are developed for natural use domains with high environmental complexity, stemming from physical influences outside of the developers’ control. This study investigates what challenges emerge from such complexity and how developers mitigate them. Drawing upon a rich longitudinal single-case study on the development of AI decision-support for maritime navigation, findings show that achieving high output accuracy is complicated by the physical environment hindering training data creation. Further, developers chose to reduce the output accuracy and adapt the HMI design to successfully situate the AI application in an existing sociotechnical context. This study contributes to IS literature following recent calls for phenomenon-based examination of emerging challenges when extending the scope frontier of AI and provides practical recommendations for developing AI decision-support for complex environments.
ItemKnowledge Transfer between Humans and Conversational Agents: A Review, Organizing Framework, and Future Directions( 2023-01-03)Conversational agents (CAs) that use natural language to interact with humans are becoming ubiquitous in our daily lives. For CAs to perform effectively, knowledge transfer between human users and CAs is vital to complete tasks and to build common understanding with humans. While such knowledge transfer is important, relatively less research attention has been paid to it. Overall, we lack a systematic overview of how knowledge transfer can be facilitated between humans and CAs. Motivated thus, this article presents a literature review of empirical IS, HCI and Communications studies on the knowledge transfer between humans and CAs. We analyzed papers on this topic, synthesized the studies based on the antecedents, directions, processes, and outcomes of knowledge transfer. We contribute by providing a systematic understanding of research on knowledge transfer in human-CA interactions, proposing an organizing framework, identifying gaps in prior work, and outlining key future research directions.
ItemContext-aware Knowledge-based Systems: A Literature Review( 2023-01-03)Context awareness systems, a subcategory of intelligent systems, are concerned with suggesting relevant products/services to users' situations as smart services. One key element for improving smart services’ quality is to organize and manipulate contextual data in an appropriate manner to facilitate knowledge generation from these data. In this light, a knowledge-based approach, can be used as a key component in context-aware systems. Context awareness and knowledge-based systems, in fact, have been gaining prominence in their respective domains for decades. However, few studies have focused on how to reconcile the two fields to maximize the benefits of each field. For this reason, the objective of this paper is to present a literature review of how context-aware systems, with a focus on the knowledge-based approach, have recently been conceptualized to promote further research in this area. In the end, the implications and current challenges of the study will be discussed.