[AISWorld] Call for Papers: Special Issue on "Hybrid Intelligence in Business Networks"

[Electronic Markets]

--- Apologies for cross-postings---

 

Electronic Markets – The International Journal on Networked Business

 

Call for Papers

 

Special Issue on "Hybrid Intelligence in Business Networks"

 

Guest Editors

* Philipp Ebel, University of St. Gallen, Switzerland:
<mailto:philipp.ebel at unisg.> philipp.ebel at unisg.ch

* Matthias Söllner, University of Kassel, Germany: soellner at uni-kassel.de
<javascript:linkTo_UnCryptMailto('wksvdy4cyovvxobJexs7ukccov8no');>  

* Jan Marco Leimeister, University of St. Gallen, Switzerland:
janmarco.leimeister at unisg.ch

* Kevin Crowston, Syracuse University, NY, USA: crowston at syr.edu
<mailto:crowston at syr.edu>  

* Gert-Jan de Vreede, University of South Florida, USA: gdevreede at usf.edu
<mailto:gdevreede at usf.edu> 

 

Theme

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:

*         Generalizable models, methodologies and theories to design and
facilitate the interaction between human intelligence and machine
intelligence in different kinds of digital production networks 

*         Approaches for a new division of labor between AI and humans in
business networks

*         Decision models for deciding whether, when and how to access human
input

*         Effectiveness of different training strategies in improving the
performance of workers for accomplishing complex business tasks 

*         Design of incentive structures that motivate actors to participate
in a network of humans and machines 

*         Approaches for increasing user acceptance of systems with AI
components

*         Collaborative work practices in which AI acts as a teammate or
facilitates human collaboration 

*         Approaches for a new division of labor in references to the task
structure and capabilities of AI and humans

*         Approaches for increasing user acceptance of new business networks
with AI components

*         Design, implementation and evaluation of exemplar instances of
Human-AI-Collaboration

*         Legal aspects of Human-AI-Collaboration in business networks

 

We encourage contributions with a broad range of methodological approaches,
including conceptual, qualitative and quantitative research. All papers
should fit the scope of Electronic Markets (for more information see
<http://www.electronicmarkets.org/about-em/scope/>
http://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 guest editors.

 

Submission

Electronic Markets is a SSCI-listed journal (IF 3.818) and requires that all
papers be original and not published or under review elsewhere. Papers must
be submitted via our the journal’s electronic submission system at
<http://elma.edmgr.com> http://elma.edmgr.com and conform to Electronic
Markets’ publication standards (see instructions and templates at
<http://www.electronicmarkets.org/authors>
http://www.electronicmarkets.org/authors). Please note that the preferred
article length is around 8,000 words, excluding references.

 

Important deadline

* Submission Deadline: May 1, 2019

 

References

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.

 

Rainer Alt and Hans-Dieter Zimmermann 

Editors-in-Chief

 

====================================================================

Electronic Markets - The International Journal on Networked Business 

====================================================================

Editors-in-Chief: Rainer Alt, Leipzig University and Hans-Dieter Zimmermann,
FHS St. Gallen, University of Applied Sciences 

Executive Editor: Maxi Herzog, Leipzig University

 

Editorial Office:

c/o Information Systems Institute

Leipzig University 

04109 Leipzig, Germany

Mail: editors at electronicmarkets.org

Phone: +49-341-9733600

 

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