@inproceedings{orue-guerrero-coma-puig-2026-signals,
title = "From Signals to Barriers: An {LLM}-Assisted Bow-Tie Framework for {NGO} Civic-Space Risk Monitoring",
author = "Oru{\'e}-Guerrero, Noem{\'i} and
Coma-Puig, Bernat",
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-1.17/",
pages = "188--197",
ISBN = "979-8-89176-495-8",
abstract = "Non-governmental organisations (NGOs) increasingly operate in environments where regulatory, financial, administrative, and reputational pressures shift fast and unpredictably. The sector{'}s standard risk tools (registers, likelihood-impact matrices, key risk indicators) might help to analyse existing risks, but they do not expose the causal chain from an early signal to its operational consequence, and they do not stay updated without maintenance that under-resourced NGO staff simply cannot provide. We tackle both problems. First, we adapt the bow-tie methodology from safety-critical industries to the NGO context, anchored on a single top event: the loss of operational viability in a country, with five threat categories and explicit barriers. Second, we instantiate an LLM-assisted maintenance system that classifies incoming signals, updates a versioned, evidence-grounded knowledge base, and flags barriers for human verification, while leaving consequential decisions to people. We illustrate the framework with a worked example from public reports (CIVICUS, ICNL), showing structured, transparent decision support as a concrete form of positive impact for NLP applied to civic resilience."
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<abstract>Non-governmental organisations (NGOs) increasingly operate in environments where regulatory, financial, administrative, and reputational pressures shift fast and unpredictably. The sector’s standard risk tools (registers, likelihood-impact matrices, key risk indicators) might help to analyse existing risks, but they do not expose the causal chain from an early signal to its operational consequence, and they do not stay updated without maintenance that under-resourced NGO staff simply cannot provide. We tackle both problems. First, we adapt the bow-tie methodology from safety-critical industries to the NGO context, anchored on a single top event: the loss of operational viability in a country, with five threat categories and explicit barriers. Second, we instantiate an LLM-assisted maintenance system that classifies incoming signals, updates a versioned, evidence-grounded knowledge base, and flags barriers for human verification, while leaving consequential decisions to people. We illustrate the framework with a worked example from public reports (CIVICUS, ICNL), showing structured, transparent decision support as a concrete form of positive impact for NLP applied to civic resilience.</abstract>
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%0 Conference Proceedings
%T From Signals to Barriers: An LLM-Assisted Bow-Tie Framework for NGO Civic-Space Risk Monitoring
%A Orué-Guerrero, Noemí
%A Coma-Puig, Bernat
%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 orue-guerrero-coma-puig-2026-signals
%X Non-governmental organisations (NGOs) increasingly operate in environments where regulatory, financial, administrative, and reputational pressures shift fast and unpredictably. The sector’s standard risk tools (registers, likelihood-impact matrices, key risk indicators) might help to analyse existing risks, but they do not expose the causal chain from an early signal to its operational consequence, and they do not stay updated without maintenance that under-resourced NGO staff simply cannot provide. We tackle both problems. First, we adapt the bow-tie methodology from safety-critical industries to the NGO context, anchored on a single top event: the loss of operational viability in a country, with five threat categories and explicit barriers. Second, we instantiate an LLM-assisted maintenance system that classifies incoming signals, updates a versioned, evidence-grounded knowledge base, and flags barriers for human verification, while leaving consequential decisions to people. We illustrate the framework with a worked example from public reports (CIVICUS, ICNL), showing structured, transparent decision support as a concrete form of positive impact for NLP applied to civic resilience.
%U https://aclanthology.org/2026.nlp4pi-1.17/
%P 188-197
Markdown (Informal)
[From Signals to Barriers: An LLM-Assisted Bow-Tie Framework for NGO Civic-Space Risk Monitoring](https://aclanthology.org/2026.nlp4pi-1.17/) (Orué-Guerrero & Coma-Puig, NLP4PI 2026)
ACL