@inproceedings{real-viola-2026-aligned,
title = "Aligned for Whom? A {B}razilian Research Agenda for Culturally Grounded {AI} Safety Evaluation",
author = "Real, Livy and
Viola, Roberta",
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-1.5/",
pages = "49--56",
ISBN = "979-8-89176-495-8",
abstract = "Frontier models were constructed within a few specific cultural and linguistic contexts and validated against a particular set of values. We argue this limitation operates at three levels: technical (safety evaluations fail silently in non-English languages), theoretical (alignment frameworks inherit an implicit universalism about human values that obscures cross-cultural variation), and governance (the power to declare a model safe remains concentrated in private laboratories unaccountable to the populations they affect). Brazilian Portuguese, spoken by over 215 million people, sits at the intersection of these three failures. We propose a Brazilian research agenda structured around three fronts: (i) culturally-grounded dangerous capability benchmarks constructed participatorily in Brazilian Portuguese; (ii) adaptive evaluation methodologies that keep evaluation responsive to evolving models and locally defined risks; and (iii) a distributed safety evaluation infrastructure in which nationally accountable institutions assess frontier models before large-scale deployment."
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<abstract>Frontier models were constructed within a few specific cultural and linguistic contexts and validated against a particular set of values. We argue this limitation operates at three levels: technical (safety evaluations fail silently in non-English languages), theoretical (alignment frameworks inherit an implicit universalism about human values that obscures cross-cultural variation), and governance (the power to declare a model safe remains concentrated in private laboratories unaccountable to the populations they affect). Brazilian Portuguese, spoken by over 215 million people, sits at the intersection of these three failures. We propose a Brazilian research agenda structured around three fronts: (i) culturally-grounded dangerous capability benchmarks constructed participatorily in Brazilian Portuguese; (ii) adaptive evaluation methodologies that keep evaluation responsive to evolving models and locally defined risks; and (iii) a distributed safety evaluation infrastructure in which nationally accountable institutions assess frontier models before large-scale deployment.</abstract>
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%0 Conference Proceedings
%T Aligned for Whom? A Brazilian Research Agenda for Culturally Grounded AI Safety Evaluation
%A Real, Livy
%A Viola, Roberta
%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 real-viola-2026-aligned
%X Frontier models were constructed within a few specific cultural and linguistic contexts and validated against a particular set of values. We argue this limitation operates at three levels: technical (safety evaluations fail silently in non-English languages), theoretical (alignment frameworks inherit an implicit universalism about human values that obscures cross-cultural variation), and governance (the power to declare a model safe remains concentrated in private laboratories unaccountable to the populations they affect). Brazilian Portuguese, spoken by over 215 million people, sits at the intersection of these three failures. We propose a Brazilian research agenda structured around three fronts: (i) culturally-grounded dangerous capability benchmarks constructed participatorily in Brazilian Portuguese; (ii) adaptive evaluation methodologies that keep evaluation responsive to evolving models and locally defined risks; and (iii) a distributed safety evaluation infrastructure in which nationally accountable institutions assess frontier models before large-scale deployment.
%U https://aclanthology.org/2026.nlp4pi-1.5/
%P 49-56
Markdown (Informal)
[Aligned for Whom? A Brazilian Research Agenda for Culturally Grounded AI Safety Evaluation](https://aclanthology.org/2026.nlp4pi-1.5/) (Real & Viola, NLP4PI 2026)
ACL