LocaliSense: Architecting Culturally Adaptive AI for Global Information Access
| dc.contributor.author | Shaikh, Rumiza Shakeel | |
| dc.date.accessioned | 2025-05-25T23:53:52Z | |
| dc.date.available | 2025-05-25T23:53:52Z | |
| dc.date.issued | 2025-05-25 | |
| dc.description | LocaliSense is a localization layer designed to adapt AI-generated content for diverse global audiences. It post-processes outputs from large language models to enhance cultural relevance, semantic clarity, and contextual resonance across regions and languages. This white paper outlines the motivation, system architecture, use cases, and design principles behind LocaliSense, with applications spanning education, healthcare, finance, civic services, and media. The project addresses critical gaps in AI accessibility by integrating regional tone adaptation, analogy transformation, and multilingual simplification. LocaliSense offers a scalable and modular solution for product teams seeking to build culturally inclusive AI experiences. | |
| dc.description.abstract | As generative AI becomes a central interface for knowledge and communication, its outputs often default to Western linguistic norms and cultural metaphors. This reduces their relevance, usability, and trustworthiness for users in emerging markets and culturally diverse regions. LocaliSense addresses this limitation by serving as a post-processing localization layer that adapts AI-generated content across any data domain to reflect regional tone, semantic simplicity, contextual relevance, and culturally appropriate analogies. This white paper introduces the design, architecture, and applications of LocaliSense across fields such as education, healthcare, finance, civic technology, and media. By integrating location metadata, analogy transformation, and multilingual simplification engines, LocaliSense enhances both user trust and comprehension in varied linguistic and cultural settings. The system offers a scalable framework for building culturally fluent AI experiences and supports the broader goal of making AI outputs universally accessible and meaningful. We explore product implications for platforms including search, generative models, enterprise tools, and civic assistants, positioning LocaliSense as a foundational infrastructure layer for globally adaptive AI design. | |
| dc.identifier.uri | https://hdl.handle.net/1813/116961 | |
| dc.language.iso | en_US | |
| dc.publisher | Cornell University | |
| dc.rights | Attribution-ShareAlike 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-sa/4.0/ | |
| dc.subject | Generative AI, Large Language Models, AI Localization, Global Product Design, Cultural Personalization, Semantic Simplification, Human-Centered AI, Metadata-Driven UX, Inclusive Technology, Context-Aware Systems | |
| dc.title | LocaliSense: Architecting Culturally Adaptive AI for Global Information Access | |
| dc.title.alternative | A Plug-and-Play AI Layer for Global User Relevance | |
| dc.type | technical report | |
| schema.accessibilityHazard | none |
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