At a time when fund managers are exploring tools that magnify their data-gathering – or investing in them – a group of artificial intelligence engineers and data scientists have demonstrated tech’s potential by using AI to read every website in the UK to gauge the level of gender bias.
Glass AI, a start-up, used its technology to read every website originating from the UK to examine the role of men and women in the British workplace.
Using AI-powered research, Glass AI found that there is “still huge gender bias”, with around 95% of receptionists, legal secretaries and care assistants being female, while 85% of investment bankers are male.
The creative industries also “remain overwhelmingly male dominated”.
The technology read and interpreted every website of the internet from the UK and compiled information on employment practices, which is being published by the Royal Statistical Society.
Key findings include:
- 82% of all CEOs, 92% of chairpersons and 73% of directors are male, which confirms statistics already highlighted by the Office for National Statistics.
- Of 108 economic sectors examined, 87% are biased towards men. Investment banking is 85% male, across all roles.
- Civil Engineering, oil and gas remain 80% male.
- Creative industries such as media, music, internet and photography also remain heavily male biased.
- The study does reveal some female dominated sectors: veterinary science is 78% female, and primary and secondary education is 71% female.
Ana-Maria Huluba, a data scientist who ran the study, said: “What makes the study even more interesting is that men and women actually appear in almost equal numbers on the web in total, with 51% male, and 49% female – which matches the ONS numbers for gender in the workplace. And yet beneath this we get this massive segregation of roles and appearance in different economic sectors. This is a complex pattern that is supportive and yet goes beyond traditional stereotypes of activity.”
Glass AI, which has built technology to monitor the entire internet for economic and social science analysis, analysed sites if they were written in English, had a UK physical address, had some description of the organisation that the AI could recognise, and had people displayed either through team pages, biographies, or roles or descriptions.
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