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Dear colleagues,
*Electronic Markets* is seeking submissions for a *Special Issue
on "Trust
in AI for electronic markets"*. Please find further details below.
Call for Papers: “Trust in AI for electronic markets”
Submission deadline: *December, 15, 2021*
Guest Editors
o Wolfgang Maass, Saarland University and German Research Center
for
Artificial Intelligence (DFKI), Germany, wolfgang.maass(at)dfki.de
o Roman Lukyanenko, HEC Montréal, Canada,
roman.lukyanenko(at)hec.ca
o Veda C. Storey, Georgia State University, USA,
vstorey@gsu.edu
Theme
Electronic markets for trading physical, as well as digital, goods
offer a
wide variety of services based on Artificial Intelligence
technologies, as
smart market services. Smart market services generate
recommendations and
predictions using Artificial Intelligence (AI) technologies on
data
available and accessible in electronic markets. For instance,
financial
high-speed trading is only feasible by smart market services that
autonomously execute transactions according to market signals
based on AI
models trained with big data. Electronic marketplaces, including
Amazon and
Alibaba, are using AI technologies to provide smart services to
consumers,
optimize logistics, analyze consumer behavior, and derive
innovative
product and service designs. Some business leaders even consider
there to
be major threats to society from sophisticated AI solutions, while
using AI
extensively for their own business. Because AI systems elude human
understanding and scrutinization, trust in AI is crucial for the
success of
smart market services, as well as other AI or machine
learning-based
systems . Gaining trust in AI begins with transparency in the
reviews of
(a) data so that biases and gaps in knowledge of a domain are
controlled,
(b) AI models and objective functions, (c) model performance and
(d)
results generated by AI models for decision making. Trust becomes
an
important factor for overcoming uncertainty on AI-based
recommendations in
general and in electronic markets in particular.
The quality of smart market services depends on shared
understanding and
conceptual models of data used for training AI models; data
quality; the
selection and training of appropriate models; and the embedding of
models
into smart market services. Providers of smart market services are
required
to build trust relationships with business and end customers based
on
limited possibilities for opening the “black boxes” of Artificial
Intelligence systems due to increased complexity of machine
learning
models. Empirical studies on trust in AI indicate heterogenous
results.
Companies and end-users appreciate benefits and opportunities
provided by
smart market services. At the same time, concerns are raised with
respect
to privacy issues and biases of data, models and algorithms.
Overly
optimistic customers might become disappointed if smart market
services do
not deliver as expected. Proof of privacy leaks and biases might
reinforce
prejudices. Both may lead to decrease of trust in AI. Challenging
research
questions are to identify which methods, indicators and
experiences have
increasing effects on trust in AI. For instance, explainable AI is
a
technical means for opening “black boxes” of AI systems,
generally, and
smart market services, specifically.
This special issue seeks contributions on trust in Artificial
Intelligence
in the context of electronic markets. Contributions that help to
understand
challenges from an economic, legal or technical perspective are
invited.
Central issues and topics
Possible topics of submissions include, but are not limited to:
o Trust behavior and AI
o Mental models, conceptual models and AI models
o Psychological and sociological factors for trust in AI
o Human-centric design of smart market services
o Explainable AI for smart market services
o Threats for trust in AI
o Frameworks for smart markets
o Business and legal aspects influencing trust in AI
o Relationships between trust and Business models with smart
market services
o Transparency of data, AI models and recommendations
o Case studies on building trust in AI
Submission:
Electronic Markets is a Social Science Citation Index
(SSCI)-listed journal
(IF 2.981 in 2019) in the area of information systems. We
encourage
original contributions with a broad range of methodological
approaches,
including conceptual, qualitative and quantitative research.
Please also
consider position papers and case studies for this special issue.
All
papers should fit the journal scope (for more information, see
www.electronicmarkets.org/about-em/scope/) and will undergo a
double-blind
peer-review process. Submissions must be made via the journal’s
submission
system and comply with the journal's formatting standards. The
preferred
average article length is approximately 8,000 words, excluding
references.
If you would like to discuss any aspect of this special issue, you
may
either contact the guest editors or the Editorial Office.
Keywords:
Trust, Interpretability, Mental Models, Conceptual Models,
Explainable AI,
Smart Market Services, Privacy, Fairness of Artificial
Intelligence,
Biases, Transparency
Important deadline
* Submission Deadline: December, 15, 2021
References
Domingos, P. (2012). A few useful things to know about machine
learning.
Communications of the ACM, 55(10), 78-87.
https://doi.org/10.1145/2347736.2347755.
Dwivedi, Y. K. et al. (2019). Artificial intelligence (AI):
Multidisciplinary perspectives on emerging challenges,
opportunities, and
agenda for research, practice and policy. International Journal of
Information Management, 57, 101994,
https://doi.org/10.1016/j.ijinfomgt.2019.08.002.https://www.sciencedirect.com/science/journal/02684012/57/supp/C
Jacovi, A., Marasovi, A., Miller, T., & Goldberg, Y. (2021).
Formalizing
trust in artificial intelligence: Prerequisites, causes and goals
of human
trust in ai. In Proceedings of the 2021 ACM Conference on
Fairness,
Accountability, and Transparency, pp. 624-635.
https://doi.org/10.1145/3442188.3445923.
Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers:
Machines vs.
humans: The impact of artificial intelligence chatbot disclosure
on
customer purchases. Marketing Science, 38(6), 937-947.
https://doi.org/10.1287/mksc.2019.1192.
Maass, W., Parsons, J., Purao, S., Storey, V. C., & Woo, C.
(2018).
Data-driven meets theory-driven research in the era of big data:
opportunities and challenges for information systems research.
Journal of
the Association for Information Systems, 19(12), 1.
https://doi.org/10.17705/1jais.00526.
Maass, W., Parsons, J., Purao, S., & Storey, V. C. (2021).
Pairing
conceptual modeling with machine learning. Data & Knowledge
Engineering,
forthcoming.
Maass, W., Storey, V. C., & Lukyanenko, R. (2021). From mental
models to
machine learning models via conceptual models. In Exploring
Modeling
Methods for Systems Analysis and Development (EMMSAD 2021),
Melbourne,
Australia, pp. 1–8.
https://doi.org/10.1007/978-3-030-79186-5_19.
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should
I trust you?"
Explaining the predictions of any classifier. In Proceedings of
the 22nd
ACM SIGKDD international conference on knowledge discovery and
data mining,
pp. 1135-1144. dx.doi.org/10.1145/2939672.2939778.
Siau, K., & Wang, W. (2018). Building trust in artificial
intelligence,
machine learning, and robotics. Cutter Business Technology
Journal, 31(2),
47-53.
Thiebes, S., Lins, S., & Sunyaev, A. Trustworthy artificial
intelligence.
Electronic Markets, 31(2021)2.
https://doi.org/10.1007/s12525-020-00441-4.
Best regards,
Rainer Alt, Hans-Dieter Zimmermann, Ramona Coia
====================================================================
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: Ramona Coia, Leipzig University Editorial
Office:
c/o Information Systems Institute
Leipzig University
04109 Leipzig, Germany
Mail:
editors@electronicmarkets.org
Phone: +49-341-9733600
http://www.electronicmarkets.org
https://www.facebook.com/ElectronicMarkets
https://twitter.com/journal_EM
https://www.springer.com/journal/12525
Journal Impact Factor 2019: 2.981
———
Univ.-Prof. Dr.-Ing. Wolfgang Maaß
German Research Center for Artificial Intelligence (DFKI)
Saarland Informatics Campus A5 4
66123 Saarbrücken, Germany
Phone: +49(0)681 302 64736
e-mail:
wolfgang.maass@dfki.de
http://www.dfki.de/web/research/sse
http://maasslab.de
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