Artificial Intelligence
Digital and AI sovereignty: four evidence-informed cases; background paper
This paper can be cited with the following reference: Benotti, L., Busaniche, B., Gómez, M. J. and Trovato, M. 2026.Digital and AI sovereignty: Four evidence-informed cases. Paris, UNESCO. Paper commissioned to support the development of policy briefs on AI governance in education. © UNESCO 2026
More information at https://www.vialibre.org.ar/en/we-presented-the-paper-digital-and-ai-sovereignty-four-evidence-informed-cases-at-unesco/
Artificial intelligence. How it is changing our world
We contributed to the publication ‘Artificial Intelligence: How It Is Changing Our World’ in the magazine ‘südlink’.
HESEIA: A community-based dataset for evaluating social biases in large language models, co-designed in real school settings in Latin America
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.
The challenge: dissecting AI in the classroom
We have encountered recurring questions in various workshops using the E.D.I.A. tool in which teachers participated. At Vía Libre, we decided to address some possible answers as a way to deepen an increasingly necessary conversation.
These are recommendations or suggestions aimed at bringing the topic of artificial intelligence closer to teachers, students, people in technology, and those of us who use it.
They are not definitive. They are a starting point for discussion.
A methodology to characterize bias and harmful stereotypes in natural language processing in Latin America
In this paper we present a methodology that spells out how social scientists, domain experts, and machine learning experts can collaboratively explore biases and harmful stereotypes in word embeddings and large language models. Our methodology uses the software we implemented, available at https://huggingface.co/spaces/vialibre/edia
A Guide to Challenges and Recommendations for the Implementation of a Fairer AI
In this document, we elaborated a list of technical recommendations for the development of Artificial Intelligence (AI) projects, specifically Machine Learning based systems. These recommendations are the result of structured interviews with people who work in practical applications of data-based systems in various roles and organizations within the Argentine technological ecosystem, and have been elaborated through the lens of our AI ethics team, composed of activists, social scientists and computer science researchers.