Challenge: 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.
Approach: They propose a moral philosophy definition of social good and a framework to evaluate the direct and indirect real-world impact of NLP tasks.
Outcome: The proposed framework evaluates the direct and indirect real-world impact of NLP tasks and adopts the methodology of global priorities research to identify priority causes for NLP research.

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Socially Responsible NLP (N18-6)

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Challenge: This tutorial will provide an overview of ethical research tools and ethical implications of language technologies.
Approach: This tutorial will provide an overview of ethical research and practical examples . it will discuss ethical tools to ensure data, algorithms, and models are socially responsible .
Outcome: This tutorial will provide an overview of ethical research tools and methods . it will discuss philosophical foundations of ethical work along with state of the art techniques .
Good Intentions Beyond ACL: Who Does NLP for Social Good, and Where? (2025.emnlp-main)

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Challenge: 20% of all papers in the ACL Anthology address social good issues . authors are more likely to do work addressing social good concerns when publishing in venues outside of ACL.
Approach: They use author- and venue-level perspectives to map the landscape of NLP4SG . they find authors are more likely to do work addressing social good concerns outside of ACL .
Outcome: The study analyzes the literature on NLP4SG and its impact on the ACL community . 20% of all papers in the anthology address social good issues, the study finds .
The Importance of Modeling Social Factors of Language: Theory and Practice (2021.naacl-main)

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Challenge: Current NLP models focus on information content while ignoring language’s social factors.
Approach: They propose that NLP systems focus on information content while ignoring language’s social factors to improve performance.
Outcome: The proposed approach improves the performance of existing systems, open up new applications, and increase fairness and usability for all users.
Values, Ethics, Morals? On the Use of Moral Concepts in NLP Research (2023.findings-emnlp)

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Challenge: Recent studies have focused on the ethical aspects of NLP, but little to no discussion of the terminology and theories underpinning those efforts and their implications.
Approach: They propose to provide an overview of some important ethical concepts stemming from philosophy and to survey the existing literature on moral NLP w.r.t. their findings show that most papers neither provide a clear definition of the terms they use nor adhere to definitions from philosophy.
Outcome: The findings show that most papers neither provide a clear definition of the terms they use nor adhere to definitions from philosophy.
Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good (2023.findings-emnlp)

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Challenge: Recent advances in natural language processing (NLP) have created a vast number of applications that are aimed at social good applications.
Approach: They propose a dataset with three tasks that can help identify NLP4SG papers and characterize the NLP landscape by: (1) identifying the papers that address a social problem, (2) mapping them to the corresponding UN Sustainable Development Goals, and (3) identifying their methods.
Outcome: The proposed dataset can help identify NLP4SG papers and characterize the NLP landscape by: (1) identifying the papers that address a social problem, (2) mapping them to the corresponding UN Sustainable Development Goals (SDGs), and (3) identifying their methods.
On learning and representing social meaning in NLP: a sociolinguistic perspective (2021.naacl-main)

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Challenge: linguistic variation allows for the expression of social meaning, information about the social background and identity of the language user.
Approach: They introduce the concept of social meaning to NLP and discuss how sociolinguistics can inform work on representation learning in NLP.
Outcome: The proposed model can be used to learn social meaning in NLP and identify key challenges.
Systematic Inequalities in Language Technology Performance across the World’s Languages (2022.acl-long)

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Challenge: Recent studies have revealed that NLP is limited to a subset of the world’s 6,500 languages.
Approach: They propose a framework for estimating the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP.
Outcome: The proposed framework estimates the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP.
Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020.acl-main)

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Challenge: 146 papers analyzing "bias" in NLP systems lack normative reasoning, we find . authors propose three recommendations for work analyzing “bias” in Nlp systems .
Approach: They propose three recommendations for analyzing "bias" in NLP systems . they propose to focus on what kinds of system behaviors are harmful, in what ways, to whom, and why .
Outcome: The proposed methods for measuring or mitigating “bias” are poorly matched to their motivations and do not engage critically with literature outside of NLP.
On Measures of Biases and Harms in NLP (2022.findings-aacl)

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Challenge: Recent studies show that natural language processing (NLP) technologies propagate societal biases about demographic groups associated with attributes such as gender, race, and nationality.
Approach: They propose a framework for harms and questions to help practitioners understand biases . they propose measurable measures to detect and mitigate biased groups .
Outcome: The proposed framework provides a framework for harms and questions for practitioners to answer to guide the development of bias measures.

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