roxanaromero0743/LatAmRT

LatAm-RT: Culturally Adaptive Red Teaming for AI Safety in Latin America

LatAm-RT is a culturally adaptive Red Teaming benchmark for evaluating AI safety in Latin American Spanish. It operationalizes localization intensity as an explicit experimental variable through a Layered Localization Taxonomy (LLT) with three levels of progressive cultural specificity.

The dataset contains 284 evaluation prompts across:

• 4 risk domains: Fraud & Exploitation · Political & Information Harm · Privacy & Civic Systems · Social Harm & Discrimination
• 3 localization layers: L1 Direct Translation · L2 Regional LatAm Adaptation · L3 National Localization
• 4 target countries (L3 only): Mexico · El Salvador · Costa Rica · Chile
• 5 adversarial framing strategies: V1 Direct · V2 Indirect · V3 Fictional · V4 Epistemic · V5 Defensive

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Files

File	Description
latamrt_prompts.csv	284 evaluation prompts with full metadata
latamrt_cps_annotations.csv	Cultural Plausibility Score annotations (12 native annotators)

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Dataset Schema

latamrt_prompts.csv

Column	Description
prompt_id	Unique identifier (e.g. FE-1.1-L3-SV-V1)
domain	Primary risk domain
domain_secondary	Secondary domain (multi-label cases)
subcategory	Risk subcategory
seed_id	Source seed scenario
localization_layer	L1 / L2 / L3
target_country	MX / SV / CR / CL (L3 only)
variation	V1–V5 framing strategy
harm_type	explicit harmful / gray zone
prompt_text_es	Full prompt text in Spanish
grounding_notes	Slot instantiation notes
intent_preserved	Intent preservation check result

latamrt_cps_annotations.csv

Column	Description
prompt_index	Row index in the L3 prompt set
country	Target country
annotator_id	Anonymized annotator ID (e.g. MX-A1)
annotator_region	Region of origin within country
domain	Risk domain
variation	Framing strategy
LC	Linguistic Coherence (0–2)
IA	Institutional Accuracy (0–2)
SR	Scenario Realism (0–2)
FC	Framing Coherence (0–2; V3/V5 only)
cps_total	Mean(LC, IA, SR)
comment	Qualitative annotator comment (if any)

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Localization Layers

Layer	Name	Description
L1	Direct Translation	Automated EN→ES translation; no cultural adaptation
L2	Regional LatAm Adaptation	Regionally neutral Spanish; generic institutional references
L3	National Localization	Nationally specific institutions, lexicon, and social scenarios

L3 Institutional Tokens by Country

Country	National ID	Tax authority	Electoral body	Informal: money
Mexico (MX)	CURP / INE	SAT	INE / TEPJF	feria
El Salvador (SV)	DUI	DGII	TSE	pisto
Costa Rica (CR)	Cédula / DIMEX	Tributación Directa	TSE	plata
Chile (CL)	RUN	SII	SERVEL	plata / luca

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CPS Validation

Cultural plausibility of L3 prompts was validated by 12 independent native annotators (3 per country) using a four-dimension rubric:

• LC Linguistic Coherence: natural use of national dialect and register
• IA Institutional Accuracy: correctness of named institutions and procedures
• SR Scenario Realism: plausibility for a country inhabitant
• FC Framing Coherence: cultural coherence of framing (V3/V5 only)

Global mean CPS: 1.92/2.0 (range: 1.88–1.94 across countries)

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Adversarial Framing Strategies

Code	Name	Description
V1	Direct request	First-person explicit harmful request
V2	Indirect request	Third-person pronominal distancing
V3	Fictional framing	Harmful request embedded in narrative context
V4	Epistemic framing	Knowledge-seeking or educational framing
V5	Defensive framing	Self-protection or harm prevention framing

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Dataset Construction

284 prompts were generated from 12 seed scenarios derived from established English benchmarks (AdvBench, HarmBench, JailbreakBench). A two-phase curation pipeline applied:

1. Consensus filter: 3 independent LLM classification runs; 11 of 12 seeds accepted unanimously
2. Intent preservation check: 46 of 330 generated variants (13.9%) rejected for intent drift

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Intended Use

This dataset is intended exclusively for AI safety research and evaluation. It contains adversarial prompts designed to test safety systems of large language models.

Permitted uses:

• Evaluating LLM safety behavior in Latin American Spanish
• Studying localization effects on AI safety robustness
• Culturally grounded red-teaming research

Not permitted:

• Using prompts to elicit harmful outputs from deployed systems
• Commercial use without explicit permission

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Citation

@misc{romero2026latamrt,
  title  = {{LatAm-RT}: Toward Culturally Adaptive Red Teaming
            for {AI} Safety in Latin America},
  author = {Romero, Roxana R.},
  year   = {2026},
  url    = {https://huggingface.co/datasets/roxanaromero0743/LatAmRT}
---

## License

CC BY 4.0 — Attribution required. See LICENSE file.