Inferring The World's Gender and Ethnic Diversity using Personal Names
The UNESCO “Internet for Trust” conference provided a great opportunity to better understand the challenges relating to the regulation of generative AI and content moderation in social media. Namsor can indeed play a role in the process of making these tool safer for users.
ChatGPT is impressive … it works like magic. But how robust are its answers, compared to recent academic results? ChatGPT can write about the Neo-Assyrian period, but what if a major archeological discovery were made that would change everything we know about this period. How long would it take for ChatGPT to adjust its knowledge?
We’ve prompted ChatGPT with some of the research questions of the paper “Editing a Woman’s Voice” (2022, Anna M. Costello, Ekaterina Fedorova, Zhijing Jin, and Rada Mihalcea).
The IOM Diaspora Mapping Toolkit analyses pros & cons for the use of big data and onomastics for Diaspora Mapping. It is based on field experience learned from several project conducted by The International Organization for Migration (IOM) using NamSor technology. One particular case study cited in the report is Skills Mapping Through Big Data: A case study of Armenian diaspora in the United States of America and France
At NamSor, since 2012, we’ve helped countries reconnect with their diaspora, using our advanced AI to recognize personal names with high accuracy, and to produce new disaggregated information. It took us a while to understand the concept of “diaspora”, how they affect foreign direct investment, remittances, brain-gain vs. brain-drain, the travel industry, real estate … We’ve asked ChatGPT “what is a diaspora”.
Top 20 papers in The Social Science Research Network (SSRN) using or citing NamSor name classification software. SSRN is a high impact journal (top 14% of journals). Some papers used NamSor to infer the gender of a personal name, some other papers to supplement subject data with race / ethnicity, or cultural heritage and ethnic origin.
Two weeks ago, we generated several portraits using DALL-E of hypothetical Fatimata SWADOGO, a Bukinabé name shared by hundreds of people in Burkina Faso, mostly in the Centre-Nord, Nord regions of the country. Today, we present some new portraits generated from personal names with the tag #thisnamedpersondoesnotexist – and we feature a “classic fail” of AI software.
We’ve used DALL-E text-to-image AI to generate portraits, based on names shared by a large number of people. This is our second blog post in our series with tag #thisnamedpersondoesnotexist, exploring how text-to-image AIs interpret personal names. We believe this project can illustrate the complexity of personal names interpretation, at the crossroads of ethnography, sociology, sociolinguistics, geography, history and, more recently machine learning.
Burkina Faso is a multi-cultural and diverse country with a rich history. In this article, we would like to explorer how personal names can be interpreted to reflect regional, ethnic appartenance within the country. Then we would like to illustrate how the use of a personal name can affect a black-box Artificial Intelligence – such as OpenAI’s DALL-E. This is a first article in our series of blog posts with tag #thisnamedpersondoesnotexist.
DebunkEU.org analysts Aleksandra Michałowska-Kubś and Jakub Kubś conducted an analysis of social media posts related to sanctions imposed on the transit of goods to Kaliningrad. They used NamSor machine learning classification to assign a likely country of origin for the social account names (real or fake).
We’ve used a non-supervised clustering algorithm to identify Russian (Ivanov, Popov), Korean (Kim, Pak) and other names in Kazakhstan and differentiate them from Kakakh names (AZAMAT ABDRAKHMANOV, ERLAN AHMETOV, AIGUL ABDRAKHMANOVA / AYGUL ABDRAKHMANOVA …)
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