Notes on Ethnic Minority Representation Data (and relevance outside the US)

Junade Ali — Sun, 18 Nov 2018 00:17:05 +0000

I was recently speaking to a friend who was considering using their company's US headquarters ethnic minority data and categorisation for setting targets for their UK satellite offices in Manchester and London. I wanted to share some data on why I think using data from another region is a bad idea, in case anyone else is facing a similar dilemma or already doing so and not realising the negative impacts of doing so.

Would be very much interested in hearing your thoughts on this, and if you think I'm drawing the wrong conclusions here.

For those involved in Data Processing or Machine Learning, you may have heard the phrase "Garbage In, Garbage Out". Regardless of the strengths of a given algorithm; if you provide questionable data, you'll get questionable predictions. I think the same applies when using this approach for driving diversity when using Americentric diversity data.

Tech companies Diversity Reports usually include groups and data with similar categorisations to: "Asian", "Black", "Latinx", "Native American", Mixed Race" and "White". For example, refer to the below chart of Uber's diversity data from 2018:

It is common practice for San Francisco based tech companies to only provide racial diversity data for the US and not their overseas offices. In some respects, similar forms of categorisation are used by some arms of the UK government. For example; the chart below of "Average Pay per Hour (£) by Ethnicity - 2017" is split between 5 total groups of ethnic minorities, but more Anglicised towards minority groups. It is worth noting here that the two minority groups occupy both the top (Indian and Chinese) and bottom rank (Pakistani/Bangladeshi) - would ordinarily be grouped together as "Asian" in Silicon Valley metrics.

A more detailed metric is GCSE attainment by race (a school qualification measuring attainment by the age of 16). In this grouping, "White Gypsy/Roma" and "White Irish Traveller" both occupy the bottom rank). This is followed by various "Black" groups, but Pakistani children underperforming the those from the categories of "Black" and "Black African". Chinese and Indian children have the highest attainment.

Note that such metrics are not comparable to the US; according to the United States Census Bureau median household income for Pakistani-Americans is $62,848 whilst for Black or African American the same metric is at $38,555. Despite a somewhat similar language between the US and the UK, diversity data is incomparable for both regions.

To conclude; I'm drawn to believe that large and inaccurate ethnic groupings risks further undermining representation of those in particular groups.

With thanks to ethnicity-facts-figures.service.gov.uk for sourcing most data here.

I'm Junade Ali, author of multiple software books and working on a PhD in theoretical computer science. Ask me anything!

Junade Ali — Wed, 09 May 2018 17:36:50 +0000

Hello! I'm Junade; by day I work at Cloudflare, focussing on running the Support Operations engineering group. By night, I work on a PhD in theoretical computer science.

I started my developer career at 16, initially working as a web dev for a mental health charity before entering the Digital Agency world; eventually working my way up to being the Lead Developer of the largest digital agency in the UK (by headcount). After leaving the agency world, I worked on mission critical road safety software for traffic sensors and signals - with code that touches hundreds of millions of road movements every day.

Despite leaving school with no qualifications (let alone a Bachelors degree), I was accepted onto a part-time Masters course at the age of 17; during the course of my studies I worked on an Operational Research project for a large security/defence company. This work led to my first academic publication and I later won the "Best Overall Masters" award, in part due to my thesis. I have accidentally found myself writing 3 books; two on software engineering and one which was co-authored on British constitutional law.

Most recently; you may have recently heard of some of my widely-reported work on creating the k-Anonymity model used on Troy Hunt's Pwned Passwords database, which allows you to quickly check if your password is exposed against more than half-a-billion leaked passwords, whilst limiting the information you share with any third-party.

You can find me on Twitter @IcyApril, check out what I work on at my Cloudflare Blog profile and my personal blog.

DEV Community: Junade Ali

Notes on Ethnic Minority Representation Data (and relevance outside the US)

I'm Junade Ali, author of multiple software books and working on a PhD in theoretical computer science. Ask me anything!