Bio

Juan N. Pava is a Research Fellow at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), where his work focuses on AI governance, digital inclusion, and the political economy of emerging technologies, particularly in the Global South. His research examines how institutions can more equitably govern technological development, with current projects spanning low-resource languages, language digitization, AI sovereignty, and accessibility.

At Stanford HAI, Juan has contributed to research and institutional initiatives at the intersection of technology and social impact, including educational programming for civil society leaders and collaborative projects on inclusive AI development. He is also a Research Assistant at Stanford’s Human Trafficking Data Lab, where he co-authors research on legal and statistical frameworks surrounding labor exploitation and human trafficking.

Juan holds a B.A. in Philosophy and Economics from New York University and will begin the Master’s in International Policy program at Stanford University in 2026. Born and raised in Colombia, his broader interests lie in political economy, international development, and the application of normative political theory to institutional design and governance.

Contact

  • Work
    jnpava@stanford.edu
    University - Staff Department: Ethics In Society Position: Research - Post-Bacc
    University - Incoming student Department: International Policy Studies Position: Graduate

Additional Info

  • Mail Code: 3099

All Publications

  • How Can AI Support Language Digitization and Digital Inclusion? Pava, J. N., Mullaney, T. S., Meinhardt, C., Gao, A., Yang, D. Stanford Institute for Human-Centered AI. 2026

    Abstract

    This white paper analyzes the varying ways AI tools can advance language digitization work, and provides recommendations for responsibly realizing the potential of AI in supporting the digital inclusion of digitally disadvantaged languages.

  • Mind the (Language) Gap: Mind the (Language) Gap: Mapping the Challenges of LLM Development in Low-Resource Language Contexts Pava, J. N., et al Stanford Institute for Human-Centered AI. 2025

    Abstract

    As large language models (LLMs) proliferate, they increasingly shape who is empowered by AI—and who is left behind. This paper maps the technical and ethical challenges of LLM development for low-resource lan-guages, which encompass over 95% of the world’s linguistic diversity yet remain marginalized in mainstream model development. We analyze three key technical approaches to addressing this divide—massively multilingual, regional multilingual, and monolingual models—and assess their trade-offs in terms of accuracy, scalability, and cultural representation. We then examine approaches to close the underlying data gap, including ma-chine translation pipelines, semi-automated strategies, and community-driven dataset creation. We ultimately argue that addressing the digital language divide is critical to ensuring that the benefits of AI are equitably distributed—and that its harms are not disproportionately borne. By offering this landscape-level overview of cur-rent efforts across model design and data creation, we aim to draw attention to this issue and provide a foundation upon which further research can build. We conclude with three high-level recommendations to AI researchers, funders, policymakers, and civil society organizations: in-crease investment in low-resource language infrastructure and collaboration; prioritize AI development initiatives that are community-driven and culturally grounded; and ensure fair practices in the collection, access, and owner-ship of language data.

  • Response to USAID's Request for Information on AI in Global Development Playbook Meinhardt, C., Friedman, T., Badi Uz Zaman, H., Zhang, D., Balbontín, R., Pava, J. N., Raman, V., Klyman, K., Schaake, M., Caers, J., Fukuyama, F. Stanford Institute for Human-Centered AI. 2024

    Abstract

    In this response to the U.S. Agency for International Development’s (USAID) request for information on the development of an AI in Global Development Playbook, scholars from Stanford HAI and The Asia Foundation call for an approach to AI in global development that is grounded in local perspectives and tailored to the specific circumstances of Global Majority countries.

  • Inspiring Action: Identifying the Social Sector AI Opportunity Gap Di Troia, S., Parli, V., Pava, J. N., Badi Uz Zaman, H., Fitzsimmons, K. Stanford Institute for Human-Centered AI. 2024

    Abstract

    This national survey was a collaboration between Stanford’s Institute for Human-Centered Artificial Intelligence and Project Evident and was conceived as a project to shed light on the current use of, interest in, and opportunity for AI in the social and education sectors. Over the last decade, AI has reshaped the commercial sector and consumer habits, resulting in significant value creation and profitability—think value created by recommendation systems in e-commerce or streaming services. As it becomes easier to include AI applications (Microsoft Copilot, Google Workspace, OpenAI GPTs) as part of the nonprofit technology stack, the social and education sectors have the same opportunity to deploy AI to create value through enhanced mission-related outcomes.