Starling bank (Leadership)
26 August 2020
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Help us crowdsource diversity data for every organisation in the world!
Help us crowdsource diversity data for every organisation in the world!
Based on 12 faces analysed on www.starlingbank.com
Gender Diversity
The binary gender distribution based on the analysed photos
50%
Women
50%
Men
Black representation
Black folks, and Black women specifically, consistently have the lowest representation in engineering and leadership positions
92%
White
0%
Black
0%
Black Female
Two factors affect the accuracy of the result: the quality of the analysed source, and the accuracy of our AI pipeline.
Our chrome extension allows users to analyse any website, but we only publish results from good sources on our public HowDiverse.is website. A good source is a page that contains photos reflective of the analysed organisation, such as their About us or Careers page, Leadership page, Linkedin company profile, cast pages for films and series etc... Our pipeline can also identify and analyse photos containing hundreds of faces such as a photo from a company event. These all are examples of sources that reflect the company's diversity (or lack of) accurately.
Assuming the source is accurate and reflective of the subject being analysed, then the second factor is our AI pipeline itself. It is not 100% accurate, but its accuracy (~97% for gender, ~75% for ethnicity) is better than most humans. So we are happy with it, especially that we also manually revise the results that have a low confidence score to improve the model and the results in the future. So it will only get better.
So we believe that we provide a very good indication of how diverse organisation are. In fact, in the absence and reluctance of many big organisations to publish such data, our crowd-sourced analysis is often the only data available, which gives the public a baseline of transparency to hold organisations accountabile regarding their DEI goals.
Last, this is only the start. We encourage organisations to "claim" their profiles, and provide their own diversity data. We are also building tools to allow organisations to gather this data by allowing their members to self-identify beyond binary gender and ethnicity. We believe that these tools - not the use of AI alone - are the future of this platform.
Our chrome extension allows users to analyse any website, but we only publish results from good sources on our public HowDiverse.is website. A good source is a page that contains photos reflective of the analysed organisation, such as their About us or Careers page, Leadership page, Linkedin company profile, cast pages for films and series etc... Our pipeline can also identify and analyse photos containing hundreds of faces such as a photo from a company event. These all are examples of sources that reflect the company's diversity (or lack of) accurately.
Assuming the source is accurate and reflective of the subject being analysed, then the second factor is our AI pipeline itself. It is not 100% accurate, but its accuracy (~97% for gender, ~75% for ethnicity) is better than most humans. So we are happy with it, especially that we also manually revise the results that have a low confidence score to improve the model and the results in the future. So it will only get better.
So we believe that we provide a very good indication of how diverse organisation are. In fact, in the absence and reluctance of many big organisations to publish such data, our crowd-sourced analysis is often the only data available, which gives the public a baseline of transparency to hold organisations accountabile regarding their DEI goals.
Last, this is only the start. We encourage organisations to "claim" their profiles, and provide their own diversity data. We are also building tools to allow organisations to gather this data by allowing their members to self-identify beyond binary gender and ethnicity. We believe that these tools - not the use of AI alone - are the future of this platform.
By Ethnicity
The ethnicity distribution based on the analysed photos
White
Black
Middle Eastern
Asian
Latino hispanic
This page was analysed on Wednesday, 26 August 2020 at 00:33
Are you affiliated with Starling bank (Leadership)?
Claim the page to be able add your organisation's diversity data (beyond binary gender and ethnicity). Once verified, we will invite you to a platform where you can enter your organisation's official diversity data and this will be prioritised on HowDiverse.is.
If you do not have the data, we are building a platform to gather it, so add your email and stay tuned.
If you do not have the data, we are building a platform to gather it, so add your email and stay tuned.
How diverse is?
Why we built HowDiverse?
This project began when a member of the nyala.dev team received an invitation from a prominent British tech unicorn to apply for one of their job openings. Despite the company's stated commitment to promoting diversity and inclusion, a review of their "about us" page revealed a significant lack of racial diversity among their employees' photos. This discovery sparked the creation of HowDiverse.is as a somewhat tongue-in-cheek side project aimed at pointing out the contradictions in the Diversity, Equity, and Inclusion (DEI) conversation within the tech industry.
We quickly realised that our side project could have a more significant impact beyond the tech industry. For those organisations genuinely dedicated to DEI, the starting point must be transparency, which in turn fosters accountability and tangible actions. This is the ultimate goal of HowDiverse.is: to develop tools and provide open data that empower a DEI dialogue that transcends mere corporate checkboxes.
We quickly realised that our side project could have a more significant impact beyond the tech industry. For those organisations genuinely dedicated to DEI, the starting point must be transparency, which in turn fosters accountability and tangible actions. This is the ultimate goal of HowDiverse.is: to develop tools and provide open data that empower a DEI dialogue that transcends mere corporate checkboxes.
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Download the Chrome ExtensionHow does it work?
- Download the Chrome Extension
- Go to a website (i.e. the careers page or who we are pages of a company) and open the extension
- We extract all the images from the page
- We automagically detect all the faces in those images
- We run gender and ethnicity analysis using Artificial Intelligence (Deep Learning)*
- We generate a report with the result.
* We also regularly check the results manually to improve the model because AI, ironically, is racist and lacks diversity.