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Towards Artificial Intelligence Augmenting Facilitation : AI Affordances in Macro-Task Crowdsourcing

Title data

Gimpel, Henner ; Graf, Vanessa ; Laubacher, Robert ; Meindl, Oliver:
Towards Artificial Intelligence Augmenting Facilitation : AI Affordances in Macro-Task Crowdsourcing.
In: Group Decision and Negotiation. (2023) .
ISSN 1572-9907

Official URL: Volltext

Project information

Project title:
Project's official title
Project's id
Projektgruppe WI Digitalisierung
No information
Projektgruppe WI Künstliche Intelligenz
No information
Projektgruppe WI Digital Life
No information
Projektgruppe WI Digital Society
No information

Abstract in another language

Crowdsourcing holds great potential: macro-task crowdsourcing can, for example, contribute to work addressing climate change. Macro-task crowdsourcing aims to use the wisdom of a crowd to tackle non-trivial tasks such as wicked problems. However, macro-task crowdsourcing is labor-intensive and complex to facilitate, which limits its efficiency, effectiveness, and use. Technological advancements in artificial intelligence (AI) might overcome these limits by supporting the facilitation of crowdsourcing. However, AI’s potential for macro-task crowdsourcing facilitation needs to be better understood for this to happen. Here, we turn to affordance theory to develop this understanding. Affordances help us describe action possibilities that characterize the relationship between the facilitator and AI, within macro-task crowdsourcing. We follow a two-stage, bottom-up approach: The initial development stage is based on a structured analysis of academic literature. The subsequent validation & refinement stage includes two observed macro-task crowdsourcing initiatives and six expert interviews. From our analysis, we derive seven AI affordances that support 17 facilitation activities in macro-task crowdsourcing. We also identify specific manifestations that illustrate the affordances. Our findings increase the scholarly understanding of macro-task crowdsourcing and advance the discourse on facilitation. Further, they help practitioners identify potential ways to integrate AI into crowdsourcing facilitation. These results could improve the efficiency of facilitation activities and the effectiveness of macro-task crowdsourcing.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Affordance; Artificial Intelligence; Facilitation; Macro-Task Crowdsourcing
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Fraunhofer Project Group Business and Information Systems Engineering
Research Institutions > Affiliated Institutes > FIM Research Center Finance & Information Management
Result of work at the UBT: No
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 07 Feb 2023 09:51
Last Modified: 07 Feb 2023 09:51