[DMANET] Call for Papers: 3rd KDD Workshop on Data Science for Social Good (DSSG 2021)

*3rd International Virtual KDD Workshop on Data Science for Social Good
(DSSG 2021)*

*Website*: https://amulyayadav.github.io/DSSG-21/
*Submission Link*: https://easychair.org/my/conference?conf=dssg21

*Important Dates:*
Paper Submission Deadline: May 20th, 2021
Notification of Acceptance/Rejection: June 10th, 2021
Workshop Date: August 14st, 2021

*Scope*

Machine Learning and Data Science have revolutionized and entered multiple
aspects of our everyday lives, yet there is a digital divide that is
broadening every single day. The BigData revolution has hit the Western
world (i.e., North America and Europe) much more significantly, as compared
to developing countries in Africa, Asia and South America. As a result,
most of the technologies that have been developed using ML and data science
solve first-world problems faced by common people in the Western world.
While products like Siri and Alexa bring a lot of value to people in the
Western world, they bring little value to people in Sub-Saharan Africa, who
struggle on a daily basis with much graver challenges, e.g., poor
sanitation, poverty, hunger, infectious diseases, etc. As a result, it is
urgent to refocus the attention of the SIGKDD community towards problems
faced by these underserved populations in developing countries.
Accordingly, there is a growing interest to ensure that current and future
data science research is used in a responsible manner for the benefit of
humanity in the developing world and among marginalized communities (i.e.,
for social good). To achieve this goal, a wide range of perspectives and
contributions are needed, spanning the full spectrum from fundamental
research to sustained deployments in the real-world. Note that problems in
these domains are characterized by small data, uncertainties, etc., hence
new fundamental research needs to be conducted by researchers in the SIGKDD
community to solve these problems.

To that end, this workshop will explore how data science research can
contribute to solving challenging problems faced by current-day
marginalized communities around the world, especially among developing
countries. For example, what role can data science research play in
promoting health, sustainable development and infrastructure security? How
can data science initiatives be used to achieve consensus among a set of
negotiating self-interested entities (e.g., finding resolutions to trade
talks between countries)? To address such questions, this workshop will
bring together researchers and practitioners across different strands of
data science research and a wide range of important real- world application
domains. The objective is to share the current state of research and
practice, explore directions for future work, and create opportunities for
collaboration. In addition, the workshop will place a special emphasis on
highlighting data science approaches for tackling the COVID-19 pandemic
(see preliminary agenda below). The organizers believe that data science
research has an important role to play in providing unique insights about
the pandemic and developing targeted responses; we encourage submissions
from both data science researchers as well as epidemiologists, health
policy researchers, and other domain experts who are interested in engaging
with the SIGKDD community.

A unique feature of our workshop is that we aim to engage and invite
non-profit organizations which already do significant work on the
upliftment of marginalized communities such as homeless youth in North
America, poor smallholder farmers in Sub-Saharan Africa, etc. We aim to
create a dialogue between data science researchers (who possess the tools
required to develop data-driven solutions which can benefit marginalized
communities) & non-profit organizations which can inform researchers about
what are the real problems that need urgent attention, and what real-world
constraints do data-driven solutions need to respect in order to have real
impact on the ground.

Our workshop's target audience consists of: (i) data science and machine
learning researchers who have used (or are currently using) their ML
research to solve important real- world problems for society's benefit in a
measurable manner; (ii) non-profit organizations who wish to explore how
data-driven solutions could help them improve their day-to-day operations
which enables them to amplify their real-world impact; (iii)
interdisciplinary researchers combining data science research with various
disciplines (e.g., social science, psychology and criminology); and (iv)
engineers and scientists from organizations who aim for social good, and
look to build real world systems using data science techniques.

*Topics of Interest*

We are interested in a broad range of research topics, both foundational
and applied. Topics of interest include, but are not limited to:

- Applications of Learning and Optimization in Societally Beneficial
Domains
- ML Approaches for COVID-19 and Epidemics
- Real-world applications of game theory for security
- Data Science for environmental crime
- Data Science for Environmental Sustainability
- Data Science for Urban Planning
- Computational Sustainability
- Data Science for Education
- Data Science for Public Health
- Data Science for International Relations
- Data Science for Democracy in the Developing World
- Explainable Artificial Intelligence and Machine Learning for Social
Good

*Submission Details*
Submission Types

- Technical Papers: Full-length research papers of up to 8 pages
(excluding references and appendices) detailing high quality work in
progress or work that could potentially be published at a major conference
in KDD format.
- Short Papers: Position or short papers of up to 4 pages (excluding
references and appendices) in KDD format that describe initial work or the
release of privacy-preserving benchmarks and datasets on the topics of
interest.


All papers must be submitted in PDF format, using the KDD-21 author kit
<https://kdd.org/kdd2021/calls/view/call-for-research-track-papers>.
Submissions should include the name(s), affiliations, and email addresses
of all authors, i.e., submissions are not double-blind. Submissions will be
refereed on the basis of technical quality, novelty, significance, and
clarity. Each submission will be thoroughly reviewed by at least two
program committee members. Submissions of papers rejected from KDD 2021
technical program are welcomed.

Regards,
Amulya Yadav
PNC Career Development Assistant Professor
Penn State University
http://amulyayadav.com
(On behalf of the DSSG21 Organizing Committee)

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