Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
Artificial intelligence (AI) increasingly supports users in their work practices in organizations. To unlock value from AI, users need to interact with AI-based systems in their decision-making processes. With increasing sophistication of AI, understanding the mechanisms influencing the cognitive fit between users and AI-based systems is crucial to ensure a successful implementation. Drawing upon literature on cognition in IS and employing a revelatory case study, we explore mechanisms enacted by data science teams in shaping a cognitive fit. We find that the data science teams enact supporting mechanisms to enable users to make sense of AI-based systems and AI “making sense” of users mental models. With this, we contribute to the body of knowledge on cognitive fit in IS by shedding light on underlying mechanisms for an effective development of AI-based systems. For organizations, these insights are valuable to overcome barriers to the successful introduction of AI-based systems.
Recommended Citation
Wu-Gehbauer, Mei; Rosenkranz, Christoph; and Hennel, Phil, "Understanding Cognition in the Development of Artificial Intelligence-based Systems: An Exploration of Cognitive Fit and Supporting Mechanisms" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 2.
https://aisel.aisnet.org/hicss-57/da/process/2
Understanding Cognition in the Development of Artificial Intelligence-based Systems: An Exploration of Cognitive Fit and Supporting Mechanisms
Hilton Hawaiian Village, Honolulu, Hawaii
Artificial intelligence (AI) increasingly supports users in their work practices in organizations. To unlock value from AI, users need to interact with AI-based systems in their decision-making processes. With increasing sophistication of AI, understanding the mechanisms influencing the cognitive fit between users and AI-based systems is crucial to ensure a successful implementation. Drawing upon literature on cognition in IS and employing a revelatory case study, we explore mechanisms enacted by data science teams in shaping a cognitive fit. We find that the data science teams enact supporting mechanisms to enable users to make sense of AI-based systems and AI “making sense” of users mental models. With this, we contribute to the body of knowledge on cognitive fit in IS by shedding light on underlying mechanisms for an effective development of AI-based systems. For organizations, these insights are valuable to overcome barriers to the successful introduction of AI-based systems.
https://aisel.aisnet.org/hicss-57/da/process/2