-------- Forwarded Message --------
Dear Colleagues,
We cordially invite you to submit your research to the special
issue on *Data
Science for Social Good *at the Journal of Association for
Information
Systems (JAIS).
*Submission deadline*: *February 15, 2021*
* Special Issue Co-Editors*
- Ahmed Abbasi, University of Notre Dame (
aabbasi@nd.edu)
- Roger H.L. Chiang, University of Cincinnati
(
roger.chiang@uc.edu)
- Jennifer Xu, Bentley University (
jxu@bently.edu)
* Scope and Focus of the Special Issue*
Data science is an interdisciplinary field that applies
mathematics,
statistics, machine learning, and data visualization techniques to
extract
insights and knowledge from data that are normally big and
encompass both
structured and unstructured formats. Jim Gray, a 1998 Turing Award
winner,
promoted data science as a new, fourth paradigm for scientific
discovery in
response to the large amounts of data generated by scientific
experiments
in many disciplines (Hey et al., 2009). In this vein, data science
complements experimental, theoretical, and computational science
as an
emerging research paradigm for understanding nature and society
(Bell et
al., 2009). The inherently interdisciplinary nature of data
science, and
the fact that it is a catalyst for business transformation and
technology
disruption, presents many research opportunities for a diverse
discipline
such as Information Systems (IS). This has spurred a call for
greater IS
research on business data science (Saar-Tsechansky 2015).
Similarly, there
is a need for IS research on the development and evaluation of
data science
artifacts (e.g., models, methods, and systems) that address
broader
societal challenges. A lingering question remains: what societal
challenges
can IS-oriented data science research contribute towards - and how
can we
conduct such research to maximize impact and relevance?
It is generally accepted that the primary goal of scientific
discovery and
technological innovation are to improve the human condition and
the overall
well-being of society. As the world deals with unprecedented
pandemics and
grapples with painful centuries-old social justice inequities, the
importance of data science for social good has once again come
front and
center. For example, the U.S. National Institute for Health's data
science
resource page lists many available datasets and computational
resources (
https://datascience.nih.gov/covid-19-open-access-resources). This
data is
being used to develop models and methods to diagnose likelihood of
infection, detect outbreak hot spots, and forecast intensive care
unit bed
capacities. Similarly, social justice projects are attempting to
democratize data science in key contexts such as crime analytics.
However,
it must be pointed out that data science for social good (DSSG) is
not
merely about applying data science techniques to data sets of
societal
importance. As a recent McKinsey report noted, data science/AI
work
exploring social good use cases cannot rely solely on a
"social-first" or
"tech-first" approach, but rather, must consider the amalgamation
of these
two perspectives (Chui et al., 2018). The IS field has noted the
importance
of taking a more holistic approach to such research that
encompasses a
socio-technical lens (Abbasi et al., 2016) spanning context,
people,
process, and technology. Accordingly, for this special issue, some
of the
major themes include:
* Novel Data Science Artifacts for Social Good*
IT artifacts include constructs, models, methods, and
instantiations
(Hevner et al., 2004). Novelty lies at the intersection of
artifact design
as well as its application (Gregor and Hevner 2013). Whereas
application
domains like health and the environment have received some
attention, many
key areas remain underexplored (Chui et al., 2018). Examples
include
education, economic empowerment, security and justice, crises
response,
infrastructure, and hunger. For DSSG, the novel data science
artifacts
include new models, methods, and systems applied to interesting
and timely
social good use cases that enhance our knowledge and understanding
of the
state-of-the-art in meaningful ways.
* Measuring Social Impact*
Data science artifacts are often evaluated and validated based on
how well
they perform across a set of well-established performance metrics
(e.g.,
accuracy and sensitivity). The importance of such metrics has been
further
amplified in recent years with the rise of data analytics
competitions,
crowd-sourcing platforms, and leaderboards. While such metrics are
important, and in some respects, they constitute the "price of
admission"
for artifact design, they often fail to consider key downstream
implications - humanistic outcomes and societal impact. This is
what some
IS scholars have described as "going the last research
mile...using
scientific knowledge and methods to address important unsolved
classes of
problems for real people with real stakes in the outcomes"
(Nunamaker et
al., 2015, p. 11). Research geared towards measuring social impact
might
include (but is not limited to) new methods, constructs, or case
studies
that enhance our understanding of how to quantify and assess the
social
impact of data science artifacts.
* Data Science Ethics and Governance Considerations*
Important data science considerations related to trust,
explainability,
bias, fairness, privacy, and ethical use are beginning to garner a
fair
amount of attention from policy makers, academia, and the business
community - and for good reason. However, much work has taken a
univariate
tunnel-vision perspective that fails to consider the interplay
between
these factors. As one example, through immersive longitudinal
field
research, we know that DSSG projects examining the efficacy of
interventions geared towards health disparate populations should
consider
the intersections between factors such as trust, bias, privacy,
and
fairness (Abbasi et al., 2018; Taylor et al., 2018). We welcome
research
that explores the complexity of ethical challenges and governance
considerations related to the application of data science in
interesting
societally impactful contexts.
* Topics of Interest*
The DSSG follows a tradition of IS research that examines how the
advancement of information technology and systems address societal
challenges such as digital divide and social inclusion. Data
science has a
great potential to provide tremendous social benefits in the
future. This
special issue advocates the need for more IS research in studying
DSSG, and
encourages the creation and evaluation of data science artifacts
to examine
and address societal challenges in a variety of contexts and
domains. In
addition, this special issue seeks to promote collaborations
between IS
researchers that are technically focused and those with more of a
social/people focus. Our hope is that this special issue sparks
in-depth
examination about where data science capabilities can be applied
to address
societal challenges in ways that are unique, thought-provoking,
and
impactful.
This JAIS special issue welcomes original research for addressing
societal
challenges in various domains and areas, including, but not
limited to, the
following:
- Crises response
- Healthcare and welfare
- Public transportation and safety
- Education and employment
- Security and law enforcement
- Urban planning and development
- Environmental protection, clean energy, and sustainability
- Not-for-profit organizations and government agencies' services
- Ethnical and social biases embedded in datasets and analytics
methods
- Social justice, disparities, inequality and poverty
* Submission Process and Timelines*
In the extended abstract and full paper submission, authors should
clearly
justify the novelty and significance of their work. We encourage
prospective authors to read the recent JAIS editorial on "What's
in a
Contribution?" to justify their research's significant and novel
contributions to the IS discipline regarding Data Science for
Social Good
(Leidner 2020). All submissions must be original and not be
published or
under review elsewhere. Papers should be submitted following the
standard
JAIS submission procedure (
http://aisel.aisnet.org/jais/). All
JAIS
submission guidelines must be met. Although optional, authors are
strongly
encouraged to contact the co-editors with a 1-3 page extended
abstract by
November 15, 2020 to evaluate research fit with the special issue.
The
co-editors also plan to organize an online paper development
workshop in
the summer of 2021. Authors of invited to submit a revision for a
second
round of review will have an opportunity to present their work at
this
workshop. The exact date and format of this online workshop will
be
determined after the first round of review.
November 15, 2020: 1-3 page extended abstract submission
February 15, 2021: Full paper submission
June 15, 2021: Notification of first round review
October 15, 2021: Revised manuscript submission
January 15, 2022: Notification of second round review
April 15, 2022: Second revision submission
July 15, 2022: Notification of final decision
*Special Issue Editorial Board *
Alan Abrahams, Virginia Tech
Victor Benjamin, Arizona State University
Michael Chau, University of Hong Kong
Maria De'Arteaga, University of Texas at Austin
Monica Garfield, Bentley University
Tomer Geva, Tel-Aviv University
Steven Johnson, University of Virginia
Brent Kitchens, University of Virginia
John Lalor, University of Notre Dame
Karl Lang, Baruch University
Raymond Lau, City University of Hong Kong
Yang Lee, Northeastern University
Xiaobai (Bob) Li, University of Massachusetts at Lowell
Ee-Peng Lim, Singapore Management University
Xiao Liu, Arizona State University
Asil Oztekin, University of Massachusetts at Lowell
Jeff Proudfoot, Bentley University
Shawn Qu, University of Notre Dame
Sagar Samtani, Indiana University
Alan Wang, Virginia Tech
Chih-Ping Wei, National Taiwan University
Kang Zhao, University of Iowa
Wenjun Zhou, University of Tennessee
* References*
Abbasi, A., Sarker, S., & Chiang, R. H. (2016). "Big Data
Research in
Information Systems: Toward an Inclusive Research Agenda," Journal
of the
Association for Information Systems, 17(2), i-xxxii.
Abbasi, A., Li, J., Clifford, G., & Taylor, H. (2018). "Make
"Fairness by
Design" Part of Machine Learning," Harvard Business Review.
Bell, G., Hey, T., & Szalay, A. (2009). "Beyond the Data
Deluge," Science.
(323:5919), 1297-1298.
Chui M. et al. (2018). "Notes from the AI Frontier: Applying AI
for Social
Good," McKinsey Global Institute.
Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004).
"Design Science in
Information Systems Research," MIS Quarterly, 28(1), 75-105.
Hey, T, Tansley, S., & Tolle, K. (2009). The Fourth Paradigm:
Data-Intensive Scientific Discovery. Microsoft Research.
Leidner, D. E. (2020). "What's in a Contributions?," Journal of
the
Association for Information Systems, 21(1), 238-245.
Nunamaker Jr, J. F., Briggs, R. O., Derrick, D. C., & Schwabe,
G. (2015).
"The Last Research Mile: Achieving both Rigor and Relevance in
Information
Systems Research," Journal of Management Information Systems,
32(3), 10-47.
Saar-Tsechansky, M. (2015). "Editor's comments: The Business of
Business
Data Science in IS journals," MIS Quarterly, 39(4), iii-vi.
Taylor, H. A., Henderson, F., Abbasi, A., & Clifford, G.
(2018).
"Cardiovascular Disease in African Americans: Innovative Community
Engagement for Research Recruitment and Impact," American Journal
of Kidney
Diseases, 72(5), S43-S46.
Jennifer Jie Xu, Professor
Computer Information Systems
Bentley University
Waltham, MA 02452
Tel: 781-891-2711