Designing Culturally Aligned AI Systems For Social Good in Non-Western Contexts

2025-09-19

AI Summary

This study addresses the insufficient sociocultural adaptability of AI systems in high-stakes domains—education, healthcare, law, and agriculture—within non-Western contexts. We propose a six-dimensional cross-cultural analytical framework—encompassing language, domain, population, institution, task, and safety—that foregrounds socio-technical co-adaptation and human-centered, interdisciplinary collaboration. Drawing on 17 cross-national expert interviews and qualitative analysis of multi-source secondary data, we integrate AI engineering rigor with domain-specific expertise to establish a collaborative paradigm. Empirical validation spans seven Global South countries and 18 linguistic environments. Key findings identify localized human capacity investment, institutional embedding, and culturally responsive design as critical enablers of safe, effective AI deployment. The study contributes a transferable methodology and empirically grounded insights for equitable AI governance and responsible innovation in resource-constrained, culturally diverse settings.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: AI for Accessibility
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

AI technologies are increasingly deployed in high-stakes domains such as education, healthcare, law, and agriculture to address complex challenges in non-Western contexts. This paper examines eight real-world deployments spanning seven countries and 18 languages, combining 17 interviews with AI developers and domain experts with secondary research. Our findings identify six cross-cutting factors - Language, Domain, Demography, Institution, Task, and Safety - that structured how systems were designed and deployed. These factors were shaped by sociocultural (diversity, practices), institutional (resources, policies), and technological (capabilities, limits) influences. We find that building AI systems required extensive collaboration between AI developers and domain experts. Notably, human resources proved more critical to achieving safe and effective systems in high-stakes domains than technological expertise alone. We present an analytical framework that synthesizes these dynamics and conclude with recommendations for designing AI for social good systems that are culturally grounded, equitable, and responsive to the needs of non-Western contexts.

Problem

• Designing culturally aligned AI systems for non-Western social good contexts
• Identifying sociocultural and institutional factors influencing AI deployment
• Ensuring equitable AI systems through human collaboration over pure technology

Innovation

• Combining interviews with secondary research across diverse deployments
• Identifying six cross-cutting sociocultural and institutional factors
• Prioritizing human collaboration over technological expertise alone