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 future work directions, and create collaboration opportunities. In addition, the workshop will emphasize highlighting data science approaches for tackling the United Nations Sustainable Development Goals.
Our workshop's target audience consists of
Social challenges of interest include, but are not limited to, public health, environmental sustainability and conservation, and human rights. Please see the Call for Papers for more details.
Dr. Rose Yu is an assistant professor at the University of California San Diego, Department of Computer Science and Engineering. She earned her Ph.D. in Computer Sciences at USC in 2017. She was subsequently a Postdoctoral Fellow at Caltech. Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data analysis, with applications to sustainability, health, and physical sciences. A particular emphasis of her research is on physics-guided AI which aims to integrate first principles with data-driven models. Among her awards, she has won Army ECASE Award, NSF CAREER Award, Hellman Fellow, Faculty Research Award from JP Morgan, Facebook, Google, Amazon, and Adobe, Several Best Paper Awards, Best Dissertation Award at USC, and was nominated as one of the 'MIT Rising Stars in EECS'.
Dr. Joshua Blumenstock is a Chancellor's Associate Professor at the U.C. Berkeley School of Information and the Goldman School of Public Policy. He is the Co-director of the Global Policy Lab and the Center for Effective Global Action. Blumenstock does research at the intersection of machine learning and empirical economics, with a focus on how novel data and technology can better address the needs of poor and vulnerable people around the world. He has a Ph.D. in Information Science and a M.A. in Economics from U.C. Berkeley, and Bachelor's degrees in Computer Science and Physics from Wesleyan University. He is a recipient of awards including the NSF CAREER award, the Intel Faculty Early Career Honor, and the U.C. Berkeley Chancellor's Award for Public Service. His work has appeared in general interest journals including Science, Nature, and Proceedings of the National Academy of Sciences, as well as top economics journals (e.g., the American Economic Review) and computer science conferences (e.g., ICML, KDD, AAAI, WWW, CHI).
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|Paper Session 1|
|8:50am-9:10am||Contributed Talk 1
Imputation is challenging and ignoring can be worse: Learnings from activity trackers
Sharut Gupta, Narayan Hegde, Srujana Merugu, Sriram Lakshminarasimhan
|9:10am-9:30am||Contributed Talk 2|
Analyzing and Predicting Low-Listenership Trends in a Large-Scale Mobile Health Program: A Preliminary Investigation
Arshika Lalan, Shresth Verma, Kumar Madhu Sudan, Amrita Mahale, Aparna Hegde, Milind Tambe, Aparna Taneja
|9:30am-9:50am||Contributed Talk 3
Attention based Remote Photoplethysmography Estimation from Facial Video with Equilibrium in Time-Frequency Supervision
Sungpil Woo, Muhammad Zubair, Sunhwan Lim, Daeyoung Kim
|9:50am-10:10am||Panel with authors
Moderator: Serina Chang
|10:30am-11:30am||KEYNOTE 1: Josh Blumenstock|
|Paper Session 2|
|1:00pm-1:20pm||Contributed Talk 4
SchoolFinder: An AI-Assisted System for Localizing Schools from Satellite Imagery -- Case Study of Sudan
Ferda Ofli, Iyke Maduako, Masoomali Fatehkia, Ji Lucas, Aye Nyein Thaw, Mohammad Amin Sadeghi, Sanjay Chawla, Do-Hyung Kim
|1:20pm-1:40pm||Contributed Talk 5
Accurate Measures of Vaccination and Concerns of Vaccine Holdouts from Web Search Logs
Serina Chang, Adam Fourney, Eric Horvitz
|1:40pm-2:00pm||Panel with authors
Moderator: Anwar Said
|2:00pm-3:00pm||KEYNOTE 2: Rose Yu|
|3:20pm-3:25pm||Lightning Talk 1
Mitigating Bias in Conversations: A Hate Speech Classifier and Debiaser with Prompts
Shaina Raza, Chen Ding, Deval Pandya
|3:25pm-3:30pm||Lightning Talk 2|
Monco: A Novel Deep Learning Pipeline to Predict Drug Candidates for the Inhibition of EZH2 Cofactors in Pediatric Neuroblastoma
Jaanak Prashar, Jaagat Prashar
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We invite papers from the broader intersection of machine learning and societal applications. Application areas are focused on, but not limited to, the United Nations Sustainable Development Goals, such as health and well-being, gender equality, affordable and clean energy, sustainable cities and communities, and climate action. Accepted papers will be included as part of the workshop proceedings of KDD; however, we do provide authors the option to opt out of formal proceedings. Therefore, a paper getting accepted and presented at this workshop does not preclude authors from submitting their work in other conferences or journals. However, the default assumption is that all camera-ready versions of the accepted papers will be uploaded to the workshop website. If any author would not like to have their camera-ready versions uploaded to the this workshop website (because of some conflict of interest), please reach out to the workshop chairs and we would be happy to accommodate such requests.
Submissions to the workshop track are single-blind, i.e., author names and affiliations should be listed.
We welcome two types of papers:
All submissions must use ACM Conference Proceeding template (two column format). Additional supplemental material focused on reproducibility can be provided. Proofs, pseudo-code, and code may also be included in the supplement, which has no explicit page limit. As in previous years, the supplementary material should be included in the same pdf file with the main manuscript. The main body of the paper should be self-contained, since reviewers are not required to read the supplementary material. The supplementary material will not be included in the proceedings.
Submission must be made through Microsoft CMT (https://cmt3.research.microsoft.com/DSSG2023).
Submissions violating these formatting requirements will be desk-rejected. The Word template guideline can be found here. The Latex/overleaf template guideline can be found here.
Be mindful of the following dates:
Note: all deadlines are AoE (Anywhere on Earth).
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