How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact

TL;DR

This paper develops a moral and impact-oriented framework to evaluate NLP tasks' societal benefits, guiding future research towards social good by combining moral philosophy and global priorities insights.

Contribution

It introduces a novel framework for assessing NLP's social impact, grounded in moral philosophy and global priorities, and offers practical guidelines for socially beneficial NLP research.

Findings

Proposes a framework to evaluate NLP impact on social good

Identifies priority causes for NLP research based on impact assessment

Provides practical guidelines for future NLP research for social good

Abstract

Recent years have seen many breakthroughs in natural language processing (NLP), transitioning it from a mostly theoretical field to one with many real-world applications. Noting the rising number of applications of other machine learning and AI techniques with pervasive societal impact, we anticipate the rising importance of developing NLP technologies for social good. Inspired by theories in moral philosophy and global priorities research, we aim to promote a guideline for social good in the context of NLP. We lay the foundations via the moral philosophy definition of social good, propose a framework to evaluate the direct and indirect real-world impact of NLP tasks, and adopt the methodology of global priorities research to identify priority causes for NLP research. Finally, we use our theoretical framework to provide some practical guidelines for future NLP research for social good. Our data and code are available at http://github.com/zhijing-jin/nlp4sg_acl2021. In addition, we curate a list of papers and resources on NLP for social good at https://github.com/zhijing-jin/NLP4SocialGood_Papers.