Amplify Initiative: Building A Localized Data Platform for Globalized AI

• URL: http://arxiv.org/abs/2504.14105v1
• Date: Fri, 18 Apr 2025 23:20:52 GMT
• Title: Amplify Initiative: Building A Localized Data Platform for Globalized AI
• Authors: Qazi Mamunur Rashid, Erin van Liemt, Tiffany Shih, Amber Ebinama, Karla Barrios Ramos, Madhurima Maji, Aishwarya Verma, Charu Kalia, Jamila Smith-Loud, Joyce Nakatumba-Nabende, Rehema Baguma, Andrew Katumba, Chodrine Mutebi, Jagen Marvin, Eric Peter Wairagala, Mugizi Bruce, Peter Oketta, Lawrence Nderu, Obichi Obiajunwa, Abigail Oppong, Michael Zimba, Data Authors,
• Abstract summary: Current AI models often fail to account for local context and language, given the predominance of English and Western internet content in their training data.<n>Amplify Initiative, a data platform and methodology, leverages expert communities to collect diverse, high-quality data to address the limitations of these models.<n>The platform is designed to enable co-creation of datasets, provide access to high-quality multilingual datasets, and offer recognition to data authors.
• Score: 3.045104054104307
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current AI models often fail to account for local context and language, given the predominance of English and Western internet content in their training data. This hinders the global relevance, usefulness, and safety of these models as they gain more users around the globe. Amplify Initiative, a data platform and methodology, leverages expert communities to collect diverse, high-quality data to address the limitations of these models. The platform is designed to enable co-creation of datasets, provide access to high-quality multilingual datasets, and offer recognition to data authors. This paper presents the approach to co-creating datasets with domain experts (e.g., health workers, teachers) through a pilot conducted in Sub-Saharan Africa (Ghana, Kenya, Malawi, Nigeria, and Uganda). In partnership with local researchers situated in these countries, the pilot demonstrated an end-to-end approach to co-creating data with 155 experts in sensitive domains (e.g., physicians, bankers, anthropologists, human and civil rights advocates). This approach, implemented with an Android app, resulted in an annotated dataset of 8,091 adversarial queries in seven languages (e.g., Luganda, Swahili, Chichewa), capturing nuanced and contextual information related to key themes such as misinformation and public interest topics. This dataset in turn can be used to evaluate models for their safety and cultural relevance within the context of these languages.

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