Towards a Future Reallocation of Work between Humans and Machines – Taxonomy of Tasks and Interaction Types in the Context of Machine Learning

Traumer, Fabian and Reiß, Sarah Oeste and Leimeister, Jan Marco (2017) Towards a Future Reallocation of Work between Humans and Machines – Taxonomy of Tasks and Interaction Types in the Context of Machine Learning. International Conference On Information (ICIS).

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Abstract

In today’s race for competitive advantages, more and more companies implement innovations in artificial intelligence and machine learning (ML). Although these machines take over tasks that have been executed by humans, they will not make human workforce obsolete. To leverage the potentials of ML, collaboration between humans and machines is necessary. Before collaboration processes can be developed, a classification of tasks in the field of ML is needed. Therefore, we present a taxonomy for the classification of tasks due to their complexity and the type of interaction. To derive insights about typical tasks and task-complexity, we conducted a literature review as well as a focus group workshop. We identified three levels of task-complexity and three types of interactions. Connecting them reveals three generic types of tasks. We provide prescriptive knowledge inherent in the task/interaction-taxonomy.

Item Type: Article
Subjects: L Education > L Education (General)
T Technology > T Technology (General)
Divisions: Faculty of Industrial Engineering and Informatics > Information System
Depositing User: staff repository 3
Date Deposited: 16 Aug 2018 19:36
Last Modified: 16 Aug 2018 19:36
URI: http://repository.ittelkom-pwt.ac.id/id/eprint/3732

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