HESEIA: A community-based dataset for evaluating social biases in large language models, co-designed in real school settings in Latin America

TL;DR

HESEIA is a participatory, community-created dataset of 46,499 sentences capturing intersectional social biases in Latin American educational contexts, designed to evaluate and improve large language models.

Contribution

This paper introduces HESEIA, a novel, community-based dataset co-designed with Latin American educators to assess social biases in language models, emphasizing local context and intersectionality.

Findings

HESEIA contains more stereotypes unrecognized by current LLMs than previous datasets.

The dataset reflects diverse demographic and educational contexts.

Participatory creation enhances relevance and cultural specificity.

Abstract

Most resources for evaluating social biases in Large Language Models are developed without co-design from the communities affected by these biases, and rarely involve participatory approaches. We introduce HESEIA, a dataset of 46,499 sentences created in a professional development course. The course involved 370 high-school teachers and 5,370 students from 189 Latin-American schools. Unlike existing benchmarks, HESEIA captures intersectional biases across multiple demographic axes and school subjects. It reflects local contexts through the lived experience and pedagogical expertise of educators. Teachers used minimal pairs to create sentences that express stereotypes relevant to their school subjects and communities. We show the dataset diversity in term of demographic axes represented and also in terms of the knowledge areas included. We demonstrate that the dataset contains more stereotypes unrecognized by current LLMs than previous datasets. HESEIA is available to support bias assessments grounded in educational communities.