Paper Type

ERF

Abstract

There is a growing realization of Artificial Intelligence (AI)’s importance, including its ability to provide competitive advantage and change work for the better. Indeed, organizations are investing in various AI applications in the hope to automate or augment human judgment. Despite the promise of AI, many organizations’ efforts with it are falling short. Therefore, adopting sensemaking theory as a theoretical lens, this study is to investigate under which conditions and how human and machines collaborations should be structured, to enhance each other’s capabilities and facilitate optimal strategical decision-making and operational effectiveness. Four types of human-machines interaction are proposed based on the level of complexity of the context and the severity of wrong decisions. Besides providing a new instrument for the analysis and assessment of human-AI interactions, this research aids the development of guidelines and facilitates the move towards explainable AI (XAI) design, development and practices.

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Aug 10th, 12:00 AM

Rethinking How Humans and Machines Make Sense Together

There is a growing realization of Artificial Intelligence (AI)’s importance, including its ability to provide competitive advantage and change work for the better. Indeed, organizations are investing in various AI applications in the hope to automate or augment human judgment. Despite the promise of AI, many organizations’ efforts with it are falling short. Therefore, adopting sensemaking theory as a theoretical lens, this study is to investigate under which conditions and how human and machines collaborations should be structured, to enhance each other’s capabilities and facilitate optimal strategical decision-making and operational effectiveness. Four types of human-machines interaction are proposed based on the level of complexity of the context and the severity of wrong decisions. Besides providing a new instrument for the analysis and assessment of human-AI interactions, this research aids the development of guidelines and facilitates the move towards explainable AI (XAI) design, development and practices.

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