DSSG-21: International KDD Workshop on Data Science for Social Good 2021 Virtual Singapore, Singapore, August 14, 2021 |
Conference website | https://amulyayadav.github.io/DSSG-21/ |
Submission link | https://easychair.org/conferences/?conf=dssg21 |
Abstract registration deadline | May 10, 2021 |
Submission deadline | May 10, 2021 |
Scope
The field of data science stands at an inflection point, and there could be many different directions in which the future of data science research could unfold. 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 (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. This workshop will explore how data science research can contribute to solving challenging problems faced by current-day societies. 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.
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) interdisciplinary researchers combining data science research with various disciplines (e.g., social science, psychology and criminology); and (iii) engineers and scientists from organizations who aim for social good, and look to build real world systems using data science techniques.
Why Now?
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. 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.
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
- Cybersecurity
- Security applications of machine learning
- Adversarial/robust learning
- Data Science for environmental crime
- Explainable Artificial Intelligence and Machine Learning
- 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
Submission Details
We solicit papers in two categories:
1. Research papers describing novel contributions in either the development of data science techniques (motivated by societal applications), or their deployment in practice. Both work in progress and recently published work will be considered. Submissions describing recently published work should clearly indicate the earlier venue and provide a link to the published paper. Papers in this category should be in standard KDD format, and at most 8 pages in length, with one additional page containing only references.
2. Position papers describing open problems or neglected perspectives on the field, proposing ideas for bringing data science methods into a new application area, or summarizing the focus areas of a group working on data science for social good. Papers in this category should be at most 4 pages (in KDD format), with one additional page containing only references.
All papers should be submitted in KDD format. Accepted papers will be selected for oral and poster presentation based on peer review. Submissions are not double-blind; the submitted paper should include author names and affiliations.