Uncovering AI Bias in Spanish: A New Evaluation Framework

Researchers introduce SESGO, a culturally-aware system to detect stereotypes in LLMs beyond English.

A new study reveals that Large Language Models (LLMs) exhibit significant cultural biases when responding in Spanish, especially within Latin American contexts. The SESGO framework, developed by researchers, offers a novel way to evaluate these biases, showing that English-optimized mitigation techniques often fail to transfer. This highlights a critical gap in fair AI development.

By Katie Rowan

September 13, 2025

4 min read

Uncovering AI Bias in Spanish: A New Evaluation Framework

Key Facts

Researchers introduced SESGO, a new framework for evaluating bias in Spanish Large Language Models (LLMs).
The evaluation focuses on culturally-aware Latin American contexts, using over 4,000 prompts.
SESGO examines bias across gender, race, socioeconomic class, and national origin.
The study found that bias mitigation techniques optimized for English do not effectively transfer to Spanish.
Bias patterns in Spanish LLMs remain consistent across different sampling temperatures.

Why You Care

Ever wonder if the AI tools you use understand more than just English? Do you think they truly grasp cultural nuances? A new study reveals a significant blind spot in how Large Language Models (LLMs) operate globally. This research, detailed in a paper titled “SESGO: Spanish Evaluation of Stereotypical Generative Outputs,” sheds light on a crucial issue. It impacts anyone using AI in diverse linguistic and cultural settings. Are your AI interactions truly unbiased, or are they perpetuating hidden stereotypes?

What Actually Happened

Researchers Melissa Robles, Catalina Bernal, Denniss Raigoso, and Mateo Dulce Rubio have introduced SESGO. This stands for Spanish Evaluation of Stereotypical Generative Outputs. The team developed a novel structure for detecting social biases in instruction-tuned LLMs, according to the announcement. Their work specifically focuses on the Spanish language within culturally-aware Latin American contexts. They highlight a essential gap in current AI evaluations. Most existing evaluations are predominantly US-English-centric, as detailed in the blog post. This leaves potential harms in other linguistic and cultural contexts largely underexamined, the paper states. The structure adapts an existing methodology by incorporating culturally-specific expressions and sayings. These encode regional stereotypes across four social categories: gender, race, socioeconomic class, and national origin.

Why This Matters to You

This research has practical implications for anyone developing or deploying AI. It shows that simply translating AI models isn’t enough to ensure fairness. Your AI might be inadvertently perpetuating stereotypes without you even knowing it. For example, imagine an AI assistant designed to help with job applications in Latin America. If it’s biased against certain national origins or socioeconomic classes, it could unfairly disadvantage users. The study proposes a new metric that combines accuracy with the direction of error. This effectively balances model performance and bias alignment in both ambiguous and disambiguated contexts, according to the announcement. This helps identify how and where bias manifests. What steps will you take to ensure your AI systems are culturally sensitive?

Key Findings from SESGO:

4,000+ prompts used: The evaluation utilized a comprehensive dataset.
4 social categories: Bias examined across gender, race, socioeconomic class, and national origin.
English mitigation fails: Bias mitigation techniques for English do not effectively transfer to Spanish tasks.
Consistent bias patterns: Bias patterns remain largely consistent across different sampling temperatures.

This work presents the first systematic evaluation examining how leading commercial LLMs respond to culturally specific bias in Spanish, the team revealed. It reveals varying patterns of bias manifestation across models, the research shows.

The Surprising Finding

Here’s the twist: many assume that if you fix bias in English, those solutions will carry over to other languages. However, the study finds this is not the case. The research shows that “bias mitigation techniques for English do not effectively transfer to Spanish tasks.” This challenges a common assumption in AI creation. It means that simply applying English-centric fixes won’t make your Spanish-speaking AI fair. What’s more, the paper states that “bias patterns remain largely consistent across different sampling temperatures.” This suggests that tweaking model creativity doesn’t necessarily reduce underlying biases. It underscores the need for culturally-specific evaluation and mitigation strategies.

What Happens Next

This research provides a crucial modular structure. It offers a natural extension to new stereotypes, bias categories, or languages and cultural contexts, as mentioned in the release. We can expect to see more targeted evaluations emerge in the next 12-24 months. These will focus on other underrepresented languages. For example, future applications might involve creating similar frameworks for African or Asian languages. This would help ensure AI systems are equitable worldwide. Developers should now prioritize culturally-aware evaluation from the outset. Your next step could be to advocate for more diverse testing datasets. This represents a significant step toward more equitable and culturally-aware evaluation of AI systems. These systems operate in the diverse linguistic environments where they are deployed, the documentation indicates.

Ready to start creating?