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The trick to effectively measure candidate demographics without biasing the process



“As I work to set up my diversity goals, how can I measure an increase in diversity of applicants when I don’t actually know if they are diverse? One cannot necessarily see diversity...and although we ask EEOC questions, we shouldn’t, rightly so, see the answers.”


This is a great question.


It’s a great idea to track metrics of success of a D&I initiative, such as the diversity of candidates and diversity of hires, but such metrics are not always straight forward to measure. Even more, it’s important to keep data such as EEOC questions (i.e. the candidate demographics) separated from the candidate screening process to avoid any real or perceived bias.


So how do you measure metrics around demographics without biasing the process?

Having cake and eating it too


The trick to this ‘having cake and eating it too’ with diversity reporting is to separate the data and the process. In other words, collect and use the demographic for reporting only without connecting it at any point to the individual going through the process. The best way to do this is to have no access to the data through the individual candidate’s record so there is no way to connect the dots.


Even better, use multiple points of reporting so you can track the demographic distribution by step – who applies, who passes initial screening, who passes the interview, etc. That will give you data on both the diversity sourcing as well as expose any adverse impact (i.e. giving an unfair advantage to one demographic over another) in the process.


Here's what the data looks like in career.place, (showing just one of the demographic graphs we have):


To effectively do this:

1) Store the data separately

Store the demographic data in a separate place from the rest of the candidate information so the hiring team actively participating in the selection process has no way to access that data.


In the above example with career.place, the data is only accessible in aggregate by organization, group of jobs, or individual job so the candidates themselves remain anonymous.

2) Do not update demographic data in real time


If the demographic report is updated in real time (i.e. it changes every time a new candidate applies or progresses through the screening process), then it’s possible to know an individual’s demographics.


For example, imagine you check the demographic report and see that there are four women who applied. Then you see a new candidate enter the process. If you check the report again and see there are now five women who applied, you know the new individual is a woman. Or if the data includes what time it was updated (timestamp), you can match the time of new candidates to the time of new data.


A simple solution is not to update the data in real-time. Use a method such as batch updates (for example, update the data after every five candidates that apply) or time-based updates (for example, update the data once a week). At career.place we use the batch method without timestamps.

3) Do not report one person


If the demographic report has a data set of 1 (there is one person who applied or one person who made it to the interview stage), then it’s possible to know an individual’s demographics.


For example, imagine you are reporting on the demographics of all candidates who apply and who pass the blind screening process. If there is only one person who has passed the blind screening and the demographic report shows one woman passed, then you know the candidate is a woman.


A simple solution is not to report on data sets of 1. In other words, if there is only one person in one of the reported states, hide it as “No Answer Provided”. At career.place, we automatically hide single data sets.

How to eat the cake


Collecting demographic data to measure a diversity metric is good. Knowing how to interpret the data is even better.


Here are a few tips for building a strategy around the demographic information.

1) Understand the available candidate pool


Before evaluating the organization’s diversity or setting diversity goals, know the demographics of the candidate pool. Diversity goals that don’t reflect the candidate pool are much harder to obtain and often lead to discrimination rather than the intended purpose.


If you are only sourcing locally, for example, the available pool could be reflected using the population’s demographics or local graduation demographics for relevant fields. If you don’t like the diversity of the candidate pool, focus on diversifying that first.


Consider, if you set a goal to hire 20% Veterans against a candidate pool that only has 10% Veterans for the field, then the goal will more likely drive the process to discriminate against those who have not served rather than promote equity for Veterans. So, if there are not enough Veterans in the field locally, consider remote positions, relocation, expand what it is to be qualified by removing requirements, or considering transferable skills.

2) Collect data where you can make a difference


Measuring diversity of those applying is a good start, but it is only part of the picture. There are plenty of other places in the hiring process that are impacted by bias, and plenty of opportunities to increase hiring with diversity and inclusion. Measuring diversity across the entire hiring process will allow you to pinpoint any potential bias challenges as well as focus your energy on where you can make the greatest impact.


For example, the data could show that you don’t have enough women applying and it’s worth focusing on attracting and sourcing more women. Or the data could show that number of women plummets in the interview stage. Without data at different stages, you could be spending all your resources attracting more women when the challenge to hiring women is actually lurking in the interview stage.

Bringing It All together


Collecting demographic data to track diversity metrics is a fantastic way to prove that your initiatives are working or know to try something different if they’re not. But, don’t let the data hinder the outcome you are working so hard to achieve. Keep demographic data separate from the hiring process, and make sure there is no way to discover an individual’s demographics from the metrics. When it comes to measuring diversity, there is a way to have your cake and eat it too.


You can do it, we can help. career.place, an anonymous candidate screening tool, has built in demographic reports providing insight into your diversity at every step through the hiring process while fully protecting the privacy and anonymity of every candidate. For more information, contact us at info@career.place.


#NoBias #diversityandinclusion


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