On the internet and in the real world women are drastically under-represented than men in all types of things whether it be profession or the entertainment industry. If a need of information arises we automatically go to Wikipedia but Wikipedia is not as diverse as you might think because women are underrepresented over there as well.
The website does not give the credit and recognition that is deserved by women because only 20% of all biographies on Wikipedia belong to women. That percentage deteriorates further when we talk about industries that have male domination like science and underrepresented historical communities.
Addressing the fact that gender bias exists especially on the internet, Angela Fan who is an AI researcher for Meta, worked on this topic for her PhD. In order to complete the research, she teamed up with her PhD advisor, Claire Gardent who is a researcher working in computer science. Together they created an AI that could generate Wikipedia style biographies in their first stage of drafts and their sources. Now they have disclosed the things they found out during the research in a paper called “Generating Full-Length Wikipedia Biographies: The Impact of Gender Bias on the Retrieval-Based Generation of Women Biographies”
Meta has also released the model and the data it requires. With this model they want to bring representation for women in science, based not only in Europe and America but also Asia and Africa focusing mainly on the latter.
The people at NLP or Natural Language Processing have been working on ways to beat gender bias in line with detecting the usage of abusive language or slurs, dialogue with pledges and translations done by machines. At Wikipedia groups like WikiProject Women, and Women in Red which is an editorial group. Both of these groups and more are working to remove the bias from the website but are not checking what factors have inserted the bias which is a crucial step in removing it as pointed out by Fan.
One of the main problems with this type of project is what is true and what is not. This places three main challenges in front of these groups, how to gather only the information that is required, how to shape that into a piece of text easy to read but still well informed and last but not the least how to find out if that information is accurate.
Read next: Alarming New Report Claims Apple & Meta Handed Over Data From Users To Hackers
The website does not give the credit and recognition that is deserved by women because only 20% of all biographies on Wikipedia belong to women. That percentage deteriorates further when we talk about industries that have male domination like science and underrepresented historical communities.
Addressing the fact that gender bias exists especially on the internet, Angela Fan who is an AI researcher for Meta, worked on this topic for her PhD. In order to complete the research, she teamed up with her PhD advisor, Claire Gardent who is a researcher working in computer science. Together they created an AI that could generate Wikipedia style biographies in their first stage of drafts and their sources. Now they have disclosed the things they found out during the research in a paper called “Generating Full-Length Wikipedia Biographies: The Impact of Gender Bias on the Retrieval-Based Generation of Women Biographies”
Meta has also released the model and the data it requires. With this model they want to bring representation for women in science, based not only in Europe and America but also Asia and Africa focusing mainly on the latter.
The people at NLP or Natural Language Processing have been working on ways to beat gender bias in line with detecting the usage of abusive language or slurs, dialogue with pledges and translations done by machines. At Wikipedia groups like WikiProject Women, and Women in Red which is an editorial group. Both of these groups and more are working to remove the bias from the website but are not checking what factors have inserted the bias which is a crucial step in removing it as pointed out by Fan.
One of the main problems with this type of project is what is true and what is not. This places three main challenges in front of these groups, how to gather only the information that is required, how to shape that into a piece of text easy to read but still well informed and last but not the least how to find out if that information is accurate.
Read next: Alarming New Report Claims Apple & Meta Handed Over Data From Users To Hackers
