Special Session: Data Science for Social Good (DS4SG 2026)

About the Special Session

The rapid explosion of digital data has enabled governments, policymakers, NGOs, and industries to develop data-driven tools that address critical societal challenges. Data has emerged as a powerful resource for generating insights and supporting real-world decision-making; however, its potential for social good remains only partially explored. This session, Data Science for Social Good, focuses on leveraging data-centric methods to improve societal outcomes in a responsible, ethical, and impactful manner. The session emphasizes two core pillars: (i) the creation and curation of high-quality, open-source datasets that enable reproducible andtransparent research, and (ii) the discovery of meaningful patterns and insights from complex data using advanced analytical and machine learning techniques. By bringing together researchers and practitioners from diverse disciplines, the workshop aims to foster collaboration and translate cutting-edge data science research into socially beneficial applications aligned with sustainability and public welfare.

We particularly welcome topics that demonstrate measurable social impact, introduce methodological advances shaped by real-world constraints, or offer critical perspectives on the limitations, risks, and unintended consequences of data-driven interventions. The scope of the session spans, but is not limited to, public health and epidemiology, climate and environmental analytics, education and learning analytics, poverty and inequality measurement, urban and regional systems, agriculture and food security, social welfare, and civic engagement. Methodological and application-driven contributions are encouraged, including interdisciplinary work that connects data science with social sciences, public policy, and ethical considerations.

Topics

Topics of interest include, but are not limited to:

Data-driven healthcare analytics, public health surveillance, and population wellbeing
Data science for climate change analysis, affordability, and environmental resilience
Learning analytics and data-driven educational systems for equitable access
Data science for social welfare, justice, fairness, and inclusion
Ethical, fair, and responsible data science for social good
Human-centered data analytics for public policy and civic engagement
Data-driven sustainability assessment and decision support
Agricultural analytics and data-driven food systems
Data science for energy systems, efficiency, and demand forecasting
Data analytics for digital humanities and cultural heritage preservation
Data-driven renewable energy integration and grid analytics
Modeling and analysis of data-informed policy-making
Fairness, accountability, transparency, and interpretability in data-driven systems
Datasets, benchmarks, platforms, tools, testbeds, and real-world case studies

Important Dates

Special Session Paper Submission: May 30, 2026 June 7, 2026 (Closed)
Special Session Paper Notification: August 10, 2026
Special Session Paper Camera-Ready: August 30, 2026
Conference Dates: October 6-9, 2026

Submission Guidelines

Authors are required to submit their papers through OpenReview at: https://openreview.net/group?id=IEEE.org/DSAA/2026/Special_Sessions
- After selecting “IEEE DSAA 2026 Special Sessions Submission”, authors should choose DS4SG 2026: Data Science for Social Good from the list of special sessions.
Paper Length & Format: Maximum 7 pages (technical content) plus additional pages solely for references, in the standard 2-column U.S. letter style of the IEEE Conference template.
Supplementary Material: Optional 2-page appendix (within the same PDF) for reproducibility details (e.g., parameters, datasets, code, proofs). Code/data release is encouraged.
Authorship: The list of authors at the time of submission is FINAL and cannot be changed.
Dual Submission: Not permitted under any circumstances. DSAA has a strict no dual submission policy.
Conflicts of Interest (COI). COIs must be declared at the time of submission in the submission system. COIs include employment at the same institution at the time of submission or in the past three years, collaborations during the past three years, advisor/advisee relationships, plus family and close friends.
Attendance. At least one author of each accepted paper must register in full and attend the conference to present the paper. No-show papers will be removed from the IEEE Xplore proceedings.
AI-Generated Text. The use of AI generated text in an article shall be disclosed in the acknowledgements section. The sections of the paper that use AI-generated text shall have a citation to the AI system used to generate the text.
All papers will be double-blind reviewed. Author names and affiliations must not appear in the submissions, and bibliographic references must be adjusted to preserve author anonymity. Submissions failing to comply with formatting or anonymity will be rejected without review.

Publication Details

All accepted Special Session papers will be published by IEEE in the DSAA main conference proceedings under its Special Session scheme and submitted for inclusion in the IEEE Xplore Digital Library.