2026
EACL
EACL 2026
NLP for Social Good: A Survey and Outlook of Challenges, Opportunities and Responsible Deployment
Abstract
AbstractNatural 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.
👥
Mega-Team
— 33 authors
🧭
Keyword Pioneer
— ethical deployment
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Vision, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Natural Language Processing, Security & Privacy
Authors
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 Nazar
,
Poli Nemkova
,
Dina Pisarevskaya
,
Naquee Rizwan
,
Nazanin Sabri
,
Keenan Samway
,
Dominik Stammbach
,
Anna Steinberg Schulten
,
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