Paper Number

1937

Paper Type

CRP

Abstract

With artificial intelligence (AI) systems increasingly augmenting users, it becomes crucial to comprehend how AI systems are developed for effective human-AI augmentation. Developing an AI system needs to consider its unique agentic, probabilistic, and self-learning properties that represent a paradigm shift from the rule-based programmed code, which is subject of traditional information systems development (ISD). We draw on a dual lens of agentic IS artifacts and cognitive fit to explore the human-AI interactions during development. Our findings suggest that the data science team plays a critical role in shaping human-AI augmentation by enacting the core alignment mechanisms “aligning the agentic IS artifact” and “aligning the user mental representation”. We found that human-AI augmentation is not pre-defined, but rather an emergent property of the triadic interaction between users, the data science team, and the AI system supported by the identified alignment mechanisms.

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Jun 14th, 12:00 AM

TOWARDS A FRAMEWORK FOR DEVELOPING ARTIFICIAL INTELLIGENCE SYSTEMS FOR HUMAN-ARTIFICIAL INTELLIGENCE AUGMENTATION

With artificial intelligence (AI) systems increasingly augmenting users, it becomes crucial to comprehend how AI systems are developed for effective human-AI augmentation. Developing an AI system needs to consider its unique agentic, probabilistic, and self-learning properties that represent a paradigm shift from the rule-based programmed code, which is subject of traditional information systems development (ISD). We draw on a dual lens of agentic IS artifacts and cognitive fit to explore the human-AI interactions during development. Our findings suggest that the data science team plays a critical role in shaping human-AI augmentation by enacting the core alignment mechanisms “aligning the agentic IS artifact” and “aligning the user mental representation”. We found that human-AI augmentation is not pre-defined, but rather an emergent property of the triadic interaction between users, the data science team, and the AI system supported by the identified alignment mechanisms.

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