Inferring The World's Gender and Ethnic Diversity using Personal Names
What are the most common surnames in Uttar Pradesh? Where is the Uttar Pradesh diaspora located ? Which casts enter the SC/ST or OBC list ? How does the caste category and affirmative action affect people’s education and employment in the public sector in Uttar Pradesh? What about
Non-discrimination laws in the US represent the only available path to challenge discrimination caused by biased AI. On the other hand, the EU’s legal framework has developed regulations to tackle the challenges posed by biased AI.
As France doesn’t collect ethnic statistics, organizations in the public and private sector need to work around the lack of data to analyze potential discriminations.
DARES, the directorate of the Ministry of Labour, Employment and Integration which produces analyses, studies and statistics on the themes of work, employment, vocational training and social dialogue has used NamSor to prepare a study on competitive exams in the public sector.
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.
CORD is a direct messaging tool for finding work. CORD’s corporate blog offers some well researched articles and recent data on representation of different genders and ethnic minorities in the Tech industry.
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).
Researchers looked at how managers communicate with financial analysts during earnings conference calls, specifically focusing on the managers’ use of
A paper just published in the British Journal of Surgery (Oxford) sourced innovative data from Dimensions AI, a large linked scientific publications dataset, and NamSor, our name gender classification tool, to explore the current state of gender diversity in global surgery – based on the top 1000 most published researchers.
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 …)
What’s in a name? Artificial intelligence was used to classify the personal names of entrepreneurs and beneficial owners of companies in eight European countries and derive unique insights into the Contribution and Challenges of Ethnic Minority Businesses in Europe.
For International Women’s Day, one blog post and one large study by European Commission illustrate how NamSor can be used to produced an analysis of the gender gap in a particular field, using open databases.
n this fresh study, “Implicit Mentoring The Unacknowledged Developer Efforts in Open Source”, Zixuan Feng, Amreeta Chatterjee, Anita Sarma, and Iftekhar Ahmed characterize implicit mentoring in software development and examine gender behavioural differences.
You must be logged in to post a comment.