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    <abstract>Socially motivated NLP research often connects technical contributions to intended benefits for people, institutions, or communities. The outcome invoked in a paper’s motivation, however, is not always the outcome evaluated in the study. This makes it difficult to determine what a result establishes, which transitions in the impact pathway remain assumptions, and how evidence should accumulate across technical, user, operational, and social studies. We introduce SOLIAS, Social Outcomes for Language-Integrated AI Systems, a reporting standard for social-impact claims in NLP. SOLIAS has three linked components: six evidence levels that identify the highest outcome layer directly evaluated, a claim-evidence alignment measure that relates conclusions to demonstrated outcomes, and a 20-item checklist for reporting populations, contexts, mechanisms, evaluation, safeguards, maintenance, and claim scope. In a retrospective audit of a multi-domain corpus of 168 research papers, demonstrated evidence is concentrated at the technical layer: 97.6% of papers are classified at Level 0, while 35.7% have a claim–evidence gap of at least two outcome layers. The audit illustrates how SOLIAS distinguishes valuable technical work from evidence about downstream outcomes without treating either as a substitute for the other. We release author and reviewer templates, operational definitions, worked examples, and a machine-readable schema to support adoption. SOLIAS is intended to make the scope of evidence explicit and to support research in which technical, user, institutional, and social outcomes are evaluated cumulatively.</abstract>
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%0 Conference Proceedings
%T SOLIAS: A Reporting Standard for Social-Impact Claims in NLP
%A Dutta, Srimonti
%A Moharir, Akshata Kishore
%Y Atwell, Katherine
%Y Borah, Angana
%Y Dementieva, Daryna
%Y Kreiss, Elisa
%Y Kotonya, Neema
%Y Liu, Jiarui
%Y Olson, Liz
%Y Wan, Ruyuan
%Y Zhao, Jieyu
%S Proceedings of the Fifth Workshop on NLP for Positive Impact (NLP4PI)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-495-8
%F dutta-moharir-2026-solias
%X Socially motivated NLP research often connects technical contributions to intended benefits for people, institutions, or communities. The outcome invoked in a paper’s motivation, however, is not always the outcome evaluated in the study. This makes it difficult to determine what a result establishes, which transitions in the impact pathway remain assumptions, and how evidence should accumulate across technical, user, operational, and social studies. We introduce SOLIAS, Social Outcomes for Language-Integrated AI Systems, a reporting standard for social-impact claims in NLP. SOLIAS has three linked components: six evidence levels that identify the highest outcome layer directly evaluated, a claim-evidence alignment measure that relates conclusions to demonstrated outcomes, and a 20-item checklist for reporting populations, contexts, mechanisms, evaluation, safeguards, maintenance, and claim scope. In a retrospective audit of a multi-domain corpus of 168 research papers, demonstrated evidence is concentrated at the technical layer: 97.6% of papers are classified at Level 0, while 35.7% have a claim–evidence gap of at least two outcome layers. The audit illustrates how SOLIAS distinguishes valuable technical work from evidence about downstream outcomes without treating either as a substitute for the other. We release author and reviewer templates, operational definitions, worked examples, and a machine-readable schema to support adoption. SOLIAS is intended to make the scope of evidence explicit and to support research in which technical, user, institutional, and social outcomes are evaluated cumulatively.
%U https://aclanthology.org/2026.nlp4pi-1.35/
%P 435-451

Markdown (Informal)

[SOLIAS: A Reporting Standard for Social-Impact Claims in NLP](https://aclanthology.org/2026.nlp4pi-1.35/) (Dutta & Moharir, NLP4PI 2026)

• SOLIAS: A Reporting Standard for Social-Impact Claims in NLP (Dutta & Moharir, NLP4PI 2026)

ACL

• Srimonti Dutta and Akshata Kishore Moharir. 2026. SOLIAS: A Reporting Standard for Social-Impact Claims in NLP. In Proceedings of the Fifth Workshop on NLP for Positive Impact (NLP4PI), pages 435–451, Budapest, Hungary. Association for Computational Linguistics.