Recent technological advances, especially in the field of machine learning, provide astonishing progress on the road towards artificial general intelligence. However, tasks in current real-world business applications cannot yet be solved by machines alone. We, therefore, identify the need for developing socio-technological ensembles of humans and machines. Such systems possess the ability to accomplish complex goals by combining human and artificial intelligence to collectively achieve superior results and continuously improve by learning from each other. Thus, the need for structured design knowledge for those systems arises. Following a taxonomy development method, this article provides three main contributions: First, we present a structured overview of interdisciplinary research on the role of humans in the machine learning pipeline. Second, we envision hybrid intelligence systems and conceptualize the relevant dimensions for system design for the first time. Finally, we offer useful guidance for system developers during the implementation of such applications.
%0 Conference Paper
%1 ls_leimeister
%A Dellermann, Dominik
%A Calma, Adrian
%A Lipusch, Nikolaus
%A Weber, Thorsten
%A Weigel, Sascha
%A Ebel, Philipp
%B Hawaii International Conference on System Sciences (HICSS)
%D 2019
%K artificial_intelligence dempub human-ai_interaction human-in-the-loop hybrid_intelligence itegpub itimpub machine_learning pub_aca pub_dde pub_nli pub_peb pub_swe u3bpub
%T The Future of Human-AI Collaboration: A Taxonomy of Design Knowledge for Hybrid Intelligence Systems
%U http://pubs.wi-kassel.de/wp-content/uploads/2020/10/JML_706_2.pdf
%X Recent technological advances, especially in the field of machine learning, provide astonishing progress on the road towards artificial general intelligence. However, tasks in current real-world business applications cannot yet be solved by machines alone. We, therefore, identify the need for developing socio-technological ensembles of humans and machines. Such systems possess the ability to accomplish complex goals by combining human and artificial intelligence to collectively achieve superior results and continuously improve by learning from each other. Thus, the need for structured design knowledge for those systems arises. Following a taxonomy development method, this article provides three main contributions: First, we present a structured overview of interdisciplinary research on the role of humans in the machine learning pipeline. Second, we envision hybrid intelligence systems and conceptualize the relevant dimensions for system design for the first time. Finally, we offer useful guidance for system developers during the implementation of such applications.
@inproceedings{ls_leimeister,
abstract = {Recent technological advances, especially in the field of machine learning, provide astonishing progress on the road towards artificial general intelligence. However, tasks in current real-world business applications cannot yet be solved by machines alone. We, therefore, identify the need for developing socio-technological ensembles of humans and machines. Such systems possess the ability to accomplish complex goals by combining human and artificial intelligence to collectively achieve superior results and continuously improve by learning from each other. Thus, the need for structured design knowledge for those systems arises. Following a taxonomy development method, this article provides three main contributions: First, we present a structured overview of interdisciplinary research on the role of humans in the machine learning pipeline. Second, we envision hybrid intelligence systems and conceptualize the relevant dimensions for system design for the first time. Finally, we offer useful guidance for system developers during the implementation of such applications.},
added-at = {2018-09-14T20:47:04.000+0200},
author = {Dellermann, Dominik and Calma, Adrian and Lipusch, Nikolaus and Weber, Thorsten and Weigel, Sascha and Ebel, Philipp},
biburl = {https://www.bibsonomy.org/bibtex/207955c47a25851cca70f2022119982da/ls_leimeister},
booktitle = {Hawaii International Conference on System Sciences (HICSS)},
eventdate = {07.01.2019-12.01.2019},
eventtitle = {Hawaii International Conference on System Sciences (HICSS)},
interhash = {aa5cee0a309ffc5c54507fdd41e2900e},
intrahash = {07955c47a25851cca70f2022119982da},
keywords = {artificial_intelligence dempub human-ai_interaction human-in-the-loop hybrid_intelligence itegpub itimpub machine_learning pub_aca pub_dde pub_nli pub_peb pub_swe u3bpub},
timestamp = {2021-07-14T14:29:24.000+0200},
title = {The Future of Human-AI Collaboration: A Taxonomy of Design Knowledge for Hybrid Intelligence Systems},
url = {http://pubs.wi-kassel.de/wp-content/uploads/2020/10/JML_706_2.pdf},
venue = {Maui, Hawaii, USA},
year = 2019
}