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Guest editors
Maxime C. Cohen; Desautels Faculty of Management, McGill
University
Nina Huang; Miami Herbert Business School, University of Miami
Meng Li, Paul A. Pavlou; C. T. Bauer College of Business,
University of Houston
Submissions open: August 1, 2022
Deadline: November 30, 2022
Motivation
Our society is experiencing a rapid digital transformation,
changing the way how different players in supply chains and
technological systems interact with each other and exert their
influences. For example, the way that businesses and customers
interact has changed in the digital economy with the influence of
computing technology and information sharing. Businesses now
routinely collect large volumes of fine-grained data to analyze
consumers' behavior, and consumers can also track changes in
firms' strategies to make informed purchasing decisions. An iconic
trend in the era of digital transformation is the increasingly
extensive use of data analytics and machine learning tools in
decision making as both strategic and operational levers. The use
of rich and large data sets also raises critical societal
concerns. For example, data sets often include personal sensitive
information that can be exploited, without explicit knowledge
and/or consent from the involved individuals, for various purposes
including monitoring, discrimination, and illegal activities. On
the one hand, data- and AI-driven algorithms may have created a
competitive advantage for firms that are using these algorithms.
For example, large corporations can create unequal competition in
the market against smaller players. Similarly, firms may attract
customers with stronger financial records by offering personalized
enticing incentives, leading to a social bias toward individuals
who are offered fewer appealing opportunities. On the other hand,
algorithms that do consider social inclusion and fairness
considerations have a great potential to reduce the inequalities
induced by social status, gender, and race, just to name a few.
Responsible data science is defined as the utilization and
exploitation of data via manual analysis or automated algorithms
(such as machine learning) that aim at improving the terms of
participation in society, particularly for individuals or
corporate entities that are disadvantaged. Such societal
participation improvements include, but are not limited to,
enhanced opportunities, increased access to resources, and greater
voice and respect for human rights.
Call for submissions
This special issue aims to attracting submissions that are closely
connected to real-world operational problems and have the
potential to impact practice from the lens of responsible data
science. All submissions must have clear managerial or theoretical
contributions, and must be built upon rigorous research methods
that serve as an appropriate framework to analyze problems:
including analytical modeling, econometric analysis, field
experimentation, and
behavioral theories.
All submissions must contribute to the operations management
literature and practice. Areas of focus include the following:
• Algorithmic bias in search and recommendation
• Price discrimination
• Social inequality
• Gender and racial inequality
• Corporate inequality and corporate social responsibility
• Discrimination in resource allocation or hiring
• Workforce relationships
• Inclusive healthcare
• Fairness, accountability, and explainability in AI
• AI standard and regulation
• Privacy concerns in data science and decision-making
• AI ethics
To fit the mission of this special issue, submitted papers should
have a solid scientific foundation and fit into one or more of the
following categories:
• Analytical: Papers well-grounded in frameworks that fall under
the category of social inclusion, ethics, fairness, and privacy.
• Empirical: Papers that use public data, proprietary data, or
experiments to test theories related to social inclusion, ethics,
fairness, and privacy.
• Technical: Papers that develop or improve upon algorithms that
address social inclusion, ethics, fairness, and privacy.
• Multimethod: Papers that combine different quantitative methods
mentioned above or qualitative approaches, such as case studies
and interviews, for triangulation purposes.
Submission process
Papers should be submitted through the POM manuscript central
website:
https://mc.manuscriptcentral.com/poms.
Specifically, please follow the prompts below(See details in the
attached file):
On the author tab, please choose "Special Issue Article" (see the
image below) in Step 1
In the drop-down menu (see the image below) that then appears in
Step 1, please select appropriate department editor: Special Issue
on Responsible Data Science.
For Step 6, please upload a cover letter that includes the title
of the special issue and the specific article type you are
submitting. Towards the end of Step 6, please indicate "yes" for
the question "Is this submission for a special issue?" and enter
the title of the special issue in the text box
below: "Responsible Data Science."
Submission guidelines
• All papers must conform to the POM's submission guidelines,
which can be found at
https://www.poms.org/journal/author_instructions.
• All authors need to follow the ethical guidelines, which can be
found at
https://www.poms.org/2021/05/poms_ethical_guidelines_for_au.html.
• We do not allow resubmission of a rejected paper in the same
department or a different department of the journal. Also, the
paper rejected in a special issue cannot be resubmitted to the
regular issue (and vice-versa).
• All papers by authors that have a conflict of interest with
either of the special issue editors will be handled by the
Editor-in-Chief and others, not by the special issue editors.
Projected Timeline
• Submissions will be accepted starting from August 1, 2022.
• First submission deadline: November 30, 2022.
• Workshop on invited papers: May 2023.
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Jason Xianghua Wu, PhD
Postdoc Research Fellow, Human-Centered AI Lab
Department of Decision & Information Sciences
C.T. Bauer College of Business, University of Houston
Email:
xwu28@central.uh.edu
Cell: (+01) 682-583-6349