@inproceedings{nemkova-etal-2026-whose,
title = "Whose Good, Whose Place? The Moral Geography of Agentic {AI} for Social Good",
author = "Nemkova, Poli and
Indukuri, Haeshitha and
Charles, Jaedon",
editor = "Atwell, Katherine and
Borah, Angana and
Dementieva, Daryna and
Kreiss, Elisa and
Kotonya, Neema and
Liu, Jiarui and
Olson, Liz and
Wan, Ruyuan and
Zhao, Jieyu",
booktitle = "Proceedings of the Fifth Workshop on {NLP} for Positive Impact ({NLP}4{PI})",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.nlp4pi-1.45/",
pages = "615--632",
ISBN = "979-8-89176-495-8",
abstract = "Agentic AI systems are increasingly proposed for social-good domains, often invoking the United Nations Sustainable Development Goals (SDGs). Yet such claims do not necessarily establish accountability to the communities these systems aim to serve. We present a structured survey of 112 papers on agentic AI for social good published between 2015 and 2026. Each paper is coded across eight dimensions using dual-LLM annotation (GPT-4o and Llama-3.3-70B) with stratified human validation on 50 papers.\par We identify a \textit{moral-geographic asymmetry}: contextual underspecification is concentrated where local context matters most. Overall, 82 of 112 papers (73{\%}) specify no geographic context. Papers aligned with health or physical/ecological SDGs name a geography 37{--}40{\%} of the time, compared with only 13{\%} for institutional and social-policy SDGs, and 0{\%} under strict coding that requires a concrete country, city, region, or population ($\chi^2(2)=8.78$, $p=.012$, Cram{\'e}r{'}s $V=.283$; strict: $\chi^2(2)=7.60$, $p=.022$, $V=.263$). The pattern is robust across seven of eight sensitivity analyses. SDG{~}16 is both the most-covered goal and the least geographically specified.\par We interpret this pattern as consistent with a form of moral abstraction: institutional good is framed more universally despite its dependence on local political, legal, and cultural conditions. Only 28 papers (25{\%}) report any real-world deployment or small-scale test. We detect no statistically significant differences in deployment rates across architecture, era, or geographic specificity. We conclude with five accountability gaps and a minimal reporting standard for more context-specific, participatory, and accountable research."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="nemkova-etal-2026-whose">
<titleInfo>
<title>Whose Good, Whose Place? The Moral Geography of Agentic AI for Social Good</title>
</titleInfo>
<name type="personal">
<namePart type="given">Poli</namePart>
<namePart type="family">Nemkova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Haeshitha</namePart>
<namePart type="family">Indukuri</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jaedon</namePart>
<namePart type="family">Charles</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fifth Workshop on NLP for Positive Impact (NLP4PI)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Katherine</namePart>
<namePart type="family">Atwell</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Angana</namePart>
<namePart type="family">Borah</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Daryna</namePart>
<namePart type="family">Dementieva</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elisa</namePart>
<namePart type="family">Kreiss</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Neema</namePart>
<namePart type="family">Kotonya</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiarui</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Liz</namePart>
<namePart type="family">Olson</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ruyuan</namePart>
<namePart type="family">Wan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jieyu</namePart>
<namePart type="family">Zhao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-495-8</identifier>
</relatedItem>
<abstract>Agentic AI systems are increasingly proposed for social-good domains, often invoking the United Nations Sustainable Development Goals (SDGs). Yet such claims do not necessarily establish accountability to the communities these systems aim to serve. We present a structured survey of 112 papers on agentic AI for social good published between 2015 and 2026. Each paper is coded across eight dimensions using dual-LLM annotation (GPT-4o and Llama-3.3-70B) with stratified human validation on 50 papers.\par We identify a moral-geographic asymmetry: contextual underspecification is concentrated where local context matters most. Overall, 82 of 112 papers (73%) specify no geographic context. Papers aligned with health or physical/ecological SDGs name a geography 37–40% of the time, compared with only 13% for institutional and social-policy SDGs, and 0% under strict coding that requires a concrete country, city, region, or population (χ²(2)=8.78, p=.012, Cramér’s V=.283; strict: χ²(2)=7.60, p=.022, V=.263). The pattern is robust across seven of eight sensitivity analyses. SDG 16 is both the most-covered goal and the least geographically specified.\par We interpret this pattern as consistent with a form of moral abstraction: institutional good is framed more universally despite its dependence on local political, legal, and cultural conditions. Only 28 papers (25%) report any real-world deployment or small-scale test. We detect no statistically significant differences in deployment rates across architecture, era, or geographic specificity. We conclude with five accountability gaps and a minimal reporting standard for more context-specific, participatory, and accountable research.</abstract>
<identifier type="citekey">nemkova-etal-2026-whose</identifier>
<location>
<url>https://aclanthology.org/2026.nlp4pi-1.45/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>615</start>
<end>632</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Whose Good, Whose Place? The Moral Geography of Agentic AI for Social Good
%A Nemkova, Poli
%A Indukuri, Haeshitha
%A Charles, Jaedon
%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 nemkova-etal-2026-whose
%X Agentic AI systems are increasingly proposed for social-good domains, often invoking the United Nations Sustainable Development Goals (SDGs). Yet such claims do not necessarily establish accountability to the communities these systems aim to serve. We present a structured survey of 112 papers on agentic AI for social good published between 2015 and 2026. Each paper is coded across eight dimensions using dual-LLM annotation (GPT-4o and Llama-3.3-70B) with stratified human validation on 50 papers.\par We identify a moral-geographic asymmetry: contextual underspecification is concentrated where local context matters most. Overall, 82 of 112 papers (73%) specify no geographic context. Papers aligned with health or physical/ecological SDGs name a geography 37–40% of the time, compared with only 13% for institutional and social-policy SDGs, and 0% under strict coding that requires a concrete country, city, region, or population (χ²(2)=8.78, p=.012, Cramér’s V=.283; strict: χ²(2)=7.60, p=.022, V=.263). The pattern is robust across seven of eight sensitivity analyses. SDG 16 is both the most-covered goal and the least geographically specified.\par We interpret this pattern as consistent with a form of moral abstraction: institutional good is framed more universally despite its dependence on local political, legal, and cultural conditions. Only 28 papers (25%) report any real-world deployment or small-scale test. We detect no statistically significant differences in deployment rates across architecture, era, or geographic specificity. We conclude with five accountability gaps and a minimal reporting standard for more context-specific, participatory, and accountable research.
%U https://aclanthology.org/2026.nlp4pi-1.45/
%P 615-632
Markdown (Informal)
[Whose Good, Whose Place? The Moral Geography of Agentic AI for Social Good](https://aclanthology.org/2026.nlp4pi-1.45/) (Nemkova et al., NLP4PI 2026)
ACL