Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

NLP for Social Good: A Survey and Outlook of Challenges, Opportunities, and Responsible Deployment

Antonia Karamolegkou, Angana Borah, Eunjung Cho, Sagnik Ray Choudhury, Martina Galletti, Pranav Gupta, Oana Ignat, Priyanka Kargupta, Neema Kotonya, Hemank Lamba, Sun-Joo Lee, Arushi Mangla, Ishani Mondal, Fatima Zahra Moudakir, Deniz Nazarova, Poli Nemkova, Dina Pisarevskaya, Naquee Rizwan, Nazanin Sabri, Keenan Samway, Dominik Stammbach, Anna Steinberg, David Tomás, Steven R Wilson, Bowen Yi, Jessica H Zhu, Arkaitz Zubiaga, Anders Søgaard, Alexander Fraser, Zhijing Jin, Rada Mihalcea, Joel R. Tetreault, Daryna Dementieva

TL;DR

This survey analyzes NLP for Social Good (NLP4SG) across nine domains aligned with SDGs and global risks, revealing uneven research emphasis with AI harms and inclusion dominating while poverty, peacebuilding, and environment remain underexplored. It documents trends in ACL Anthology, highlighting methodological shifts toward evaluation, data curation, and responsible deployment, and identifies key challenges such as data scarcity, bias, and safety concerns. The paper offers a forward-looking agenda emphasizing multilingual and multicultural learning, human–AI collaboration, participatory design, retrieval-augmented methods, and explainability to ensure equitable, real-world impact. Its call to action urges joint benchmarks, domain-expert partnerships, and sustainable NLP solutions that augment human expertise and address global development needs.

Abstract

Natural language processing (NLP) now shapes many aspects of our world, yet its potential for positive social impact is underexplored. This paper surveys work in ``NLP for Social Good" (NLP4SG) across nine domains relevant to global development and risk agendas, summarizing principal tasks and challenges. We analyze ACL Anthology trends, finding that inclusion and AI harms attract the most research, while domains such as poverty, peacebuilding, and environmental protection remain underexplored. Guided by our review, we outline opportunities for responsible and equitable NLP and conclude with a call for cross-disciplinary partnerships and human-centered approaches to ensure that future NLP technologies advance the public good.

NLP for Social Good: A Survey and Outlook of Challenges, Opportunities, and Responsible Deployment

TL;DR

This survey analyzes NLP for Social Good (NLP4SG) across nine domains aligned with SDGs and global risks, revealing uneven research emphasis with AI harms and inclusion dominating while poverty, peacebuilding, and environment remain underexplored. It documents trends in ACL Anthology, highlighting methodological shifts toward evaluation, data curation, and responsible deployment, and identifies key challenges such as data scarcity, bias, and safety concerns. The paper offers a forward-looking agenda emphasizing multilingual and multicultural learning, human–AI collaboration, participatory design, retrieval-augmented methods, and explainability to ensure equitable, real-world impact. Its call to action urges joint benchmarks, domain-expert partnerships, and sustainable NLP solutions that augment human expertise and address global development needs.

Abstract

Natural language processing (NLP) now shapes many aspects of our world, yet its potential for positive social impact is underexplored. This paper surveys work in ``NLP for Social Good" (NLP4SG) across nine domains relevant to global development and risk agendas, summarizing principal tasks and challenges. We analyze ACL Anthology trends, finding that inclusion and AI harms attract the most research, while domains such as poverty, peacebuilding, and environmental protection remain underexplored. Guided by our review, we outline opportunities for responsible and equitable NLP and conclude with a call for cross-disciplinary partnerships and human-centered approaches to ensure that future NLP technologies advance the public good.

Paper Structure

This paper contains 62 sections, 14 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Healthcare
  3. Mental Health.
  4. Physical Health.
  5. Challenges:
  6. Opportunities:
  7. Education
  8. Challenges:
  9. Opportunities:
  10. Poverty
  11. Challenges:
  12. Opportunities:
  13. Peacebuilding
  14. Human Rights Violations.
  15. Conflict Prediction.
  16. ...and 47 more sections

Figures (14)

  • Figure 1: Mapping NLP applications for Social Good (NLP4SG) with global goals and risks.
  • Figure 2: Number of ACL Anthology papers per domain showing publication volume and growth trajectories over time (2019–2025).
  • Figure 3: Co-occurrence heatmap of annotated NLP domains.
  • Figure 4: Overview of the normalized NLP domain distributions for conference papers ($n=32,886$) and workshop papers ($n=12,723$) from 2019 to 2024.
  • Figure 5: Prompt used to annotate ACL Anthology papers with the key NLP research directions.
  • ...and 9 more figures