Call for Papers
CfP Special Issue on "Hybrid Intelligence in Business Networks"
Submission Deadline: May 1, 2019
- Gert-Jan de Vreede, University of South Florida, USA
- Kevin Crowston, Syracuse University, NY, USA
- Jan Marco Leimeister, University of St. Gallen, Switzerland
- Matthias Söllner, University of Kassel, Germany
- Philipp Ebel, University of St. Gallen, Switzerland
New technological innovations enable the development of productive AI solutions that provide compelling benefits in various fields of application. As of now, artificial intelligence systems reached a level of productivity where they do have the potential to reduce business costs, enhance business analytics, and improve the quality of managerial decisions. Leading technology firms such as Google, Apple, Microsoft or IBM are making huge investments in AI systems, to create additional value for their customers. In sum, the investments of silicon valley’s most prominent firms in AI technologies has quadrupled from 2010 to 2015, and now reaches approximately $8.5 billion (Economist 2016). As a consequence, there is a broad consensus that artificial intelligence has the potential to deliver huge economic benefits for consumers and companies (Jordan und Mitchell 2015).
Despite these recent advances, the development of human-level, general AI in the next decades is rather doubted. Instead, the concept of hybrid intelligence gained increasing popularity in recent years (Horvitz 2014). This concept aims at using the complementary strengths of human intelligence and AI to behave more intelligent than each of the two could be in separation (Kamar 2016). While machines are particularly good in consistently solving repetitive tasks that require the fast procession of huge amount of data, humans have superior capabilities for emphatic or intuitive tasks. Therefore, artificial intelligence rather augments the human judgement through providing predictive assistance. In such settings, where AI provides the human with input that is then evaluated to make a judgement, human and machines act as teammates. Vice versa, AI systems can benefit and learn from human input. This approach allows to integrate human domain knowledge in the AI to design, complement and evaluate the capabilities of machine intelligence.
In this regard, hybrid intelligence systems constitute digital networks in which different research questions, such as task specification, creation of incentives, task allocation, quality assessment, task aggregation, and compensation mechanisms have to be addressed. By now, articles in the field have concentrated on the technological issues that are related to the development of hybrid intelligent systems and highlight the novelty character of this concept (Cheng und Bernstein 2015; Kamar 2016). Additional insights into the relationships between design decisions, actors’ behavior, and business outcomes therefore constitutes a promising avenue for further research that deserves to be addressed by researchers and practitioners.
Therefore, the time is now to call for theoretical and empirical underpinnings of hybrid intelligence can be utilized.
Central issues and themes
Possible topics of submissions include, but are not limited to:
- Legal aspects of Human-AI-Collaboration in business Networks
- Design, implementation and evaluation of exemplar instances of Human-AI-Collaboration
- Approaches for increasing user acceptance of new business networks with AI components
- Approaches for a new division of labor in references to the task structure and capabilities of AI and humans
- Collaborative work practices in which AI acts as a teammate or facilitates human collaboration
- Approaches for increasing user acceptance of systems with AI components
- Design of incentive structures that motivate actors to participate in a network of humans and machines
- Effectiveness of different training strategies in improving the performance of workers for accomplishing complex business Tasks
- Decision models for deciding whether, when and how to access human Input
- Approaches for a new division of labor between AI and humans in business Networks
- Generalizable models, methodologies and theories to design and facilitate the interaction between human intelligence and machine intelligence in different kinds of digital production Networks
We welcome these and other topics and encourage contributions with a broad range of methodological approaches, including conceptual, qualitative and quantitative research. All papers should fit Electronic Markets’ scope (www.electronicmarkets.org/about-em/scope/) and will undergo a double-blind peer review process. If you would like to discuss any aspect of the special issue, please contact the special issue editors about the fit prior to submission.
Electronic Markets is a SSCI-listed journal (IF 3.818) and requires that all papers must be original and not published or under review elsewhere. Papers must be submitted via our electronic submission system at elma.edmgr.com and conform to Electronic Markets publication standards (see instructions and templates at www.electronicmarkets.org/authors). Please note that the preferred article length is around 8,000 words.
Submission Deadline: May 1, 2019
- Philipp Ebel (University of St. Gallen, Switzerland) philipp.ebel(at)unisg.ch
- Matthias Söllner (University of Kassel, Germany) soellner(at)uni-kassel.de
- Jan Marco Leimeister (University of St. Gallen, Switzerland) janmarco.leimeister(at)unisg.ch
- Kevin Crowston (Syracuse University, NY, USA) crowston(at)syr.edu
- Gert-Jan de Vreede (University of South Florida, USA) gdevreede(at)usf.edu
Cheng, J. & Bernstein, M.S. (2015). Flock: Hybrid Crowd-Machine Learning Classifiers. Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, ACM.
Economist (2016). Artificial Intelligence - Million-Dollar Babies. April, 2nd, https://www.economist.com/business/2016/04/02/million-dollar-babies.
Horvitz, E. (2014). One Hundred Year Study on Artificial Intelligence: Reflections and Framing, Oxford University Research Paper.
Jordan, M. I. & Mitchell, T.M. (2015). Machine Learning: Trends, Perspectives, and Prospects. Science 349(6245): 255-260.
Kamar, E. (2016). Directions in Hybrid Intelligence: Complementing AI Systems with Human Intelligence. Proceedings of the International Joint Conference on Artificial Intelligence, New York.