How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact (2106.02359v3)

Abstract: Recent years have seen many breakthroughs in 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.

• The paper introduces a moral framework based on deontology, consequentialism, and virtue ethics to assess NLP tasks' societal impacts.
• It categorizes NLP research into four stages and employs causal models to analyze both direct and indirect global effects.
• The study advocates aligning NLP innovations with UN Sustainable Development Goals to maximize tangible social good.

Assessing NLP Through the Lens of Social Impact

The paper "How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact" offers a comprehensive exploration of NLP by examining its applications and potential social impacts. The authors, hailing from prominent institutions such as the Max Planck Institute, MIT, Oxford, ETH Zürich, and the University of Michigan, delve into the intersection of NLP and societal needs, advocating for research directions that prioritize social good.

Overview

The paper begins by acknowledging significant advancements in NLP, citing its transition from a theoretical discipline to one actively shaping real-world applications. The authors highlight the dual nature of NLP's societal influence, observing both beneficial applications and unintended negative consequences. Noteworthy examples include the use of NLP during the COVID-19 pandemic to assist in managing and disseminating health information, contrasted against issues like privacy leaks and bias present in systems like GPT-3.

Theoretical and Practical Framework

The central thesis of the paper stresses the importance of developing NLP technologies aligned with moral philosophy and global priorities. The authors propose a framework—grounded in moral philosophy—to evaluate NLP tasks based on their direct and indirect real-world impacts. By leveraging concepts from global priorities research, they outline a methodology to identify priority causes for NLP research. The primary goal is to determine the optimal NLP technology for researchers to maximize social good impact.

The framework is divided into several components:

• Moral Philosophies: Drawing on deontology, consequentialism, and virtue ethics, the paper contextualizes the challenges of defining social good and provides a philosophical basis for evaluating NLP impacts.
• Classification of NLP Tasks: The authors categorize NLP research into four stages: fundamental theories, building block tools, applicable tools, and deployed applications. This classification aids in the assessment of both immediate and downstream social impacts.
• Impact Evaluation: They propose structural causal models to estimate the societal impacts of NLP technologies, accounting for both direct and indirect influences.
• Research Priority Setting: Utilizing the Important/Neglected/Tractable (INT) framework, the paper outlines strategies to guide the allocation of resources towards NLP for social good.

Implications and Future Directions

The paper has far-reaching implications, suggesting that NLP research should be more than an academic exercise; it should contribute tangible benefits to society. By highlighting alignment with the United Nations Sustainable Development Goals (SDGs), the authors challenge the NLP community to consider broader societal outcomes in their research endeavors.

Future research could focus on metrics to quantify the social impact of NLP, emphasizing areas like poverty alleviation, healthcare enhancement, and educational access. The paper's recommendations also encourage transparency and responsibility among NLP developers to preemptively address ethical concerns.

Conclusion

This paper positions itself as an essential reading for researchers in NLP, urging them to align their work with global social good priorities. It also stresses the iterative nature of this pursuit, advocating for continuous evaluation and refinement of ethical frameworks in NLP. By linking existing research practices with pressing societal needs, the authors provide a roadmap for maximizing the positive impact of NLP technologies.