Equity-Aware Explainable AI for Funding Allocation in Digital Public Goods: Addressing Hate Speech in Latin America

Digital public goods aimed at mitigating hate speech and harmful online content increasingly receive public and philanthropic funding. However, funding allocation processes remain largely qualitative, English-centric, and insufficiently equipped to evaluate region-specific risks and impact potential, particularly in underrepresented ecosystems such as Latin America. As a result, initiatives grounded in local linguistic, cultural, and sociopolitical realities may be undervalued, while unintended amplification risks remain insufficiently assessed. This paper proposes an equity-aware, explainable NLP-based decision-support framework to assist funding allocation for digital public goods addressing hate speech in Latin America. The system integrates Retrieval-Augmented Generation (RAG), structured harm modeling, and rationale-guided explanations to assess: (1) projected harm mitigation capacity, (2) amplification and governance risks, and (3) distributional fairness across linguistic and regional communities. A central contribution is the introduction of fairness-aware funding simulations that evaluate how AI-assisted allocation may differentially impact Portuguese- and Spanish-speaking ecosystems, marginalized groups, and locally developed moderation infrastructures. The framework is designed as a human-in-the-loop tool, prioritizing transparency, accountability, and contextual sensitivity. By embedding explainable and equity-centered AI into funding governance processes, this work aims to strengthen the effectiveness and fairness of digital public goods funding in Latin America while offering a model adaptable to other Global South contexts.

Key Terms: Trustworthy NLP; Explainable AI; Hate Speech; Digital Public Goods; Algorithmic Fairness; Funding Allocation; Underrepresented Communities; Latin America.