What is blind hiring? And why should you do it?
‘Blind hiring’ or anonymous hiring is the practice of removing identifying information from candidates’ applications.
This usually includes:
- Dates of Birth
Why anonymize hiring?
As humans, we’re all prone to unconscious biases - every last one of us.
Every day, our brains make 1000s of micro-decisions.
To lighten our mental load, we use two systems for decision making. One for fast, intuitive decisions and one for less frequent, more effortful decisions.
System 1 is extremely useful for making everyday decisions without requiring too much bandwidth… like being on autopilot.
We use information subconscious stored away to draw quick conclusions and make decisions. This information can be from our past experiences, what we’ve seen in the media and even stereotypes.
Although necessary, System 1 can lead to negative outcomes when it comes to people decisions since it relies on shortcuts.
As a result, candidates from minority backgrounds are disproportionately overlooked.
The more we know about a person, the more grounds for bias there are.
So, the most effective means of reducing bias is to simply remove this information from applications altogether.
What does blind hiring software actually do?
Whilst all blind hiring software works slightly differently, we designed the Applied Platform to focus solely on candidates’ skills, rather than background.
We do this by going beyond anonymization - replacing traditional, biased hiring practices with predictive assessments.
All blind hiring software will strip identifying information from candidates’ applications so that they remain anonymous - at least until the interview stage.
Once you’ve anonymized a CV, what are you left with?
Someone’s education and previous work experience.
However, these are just proxies for the skills we’re really looking for - and pretty lousy ones at that.
‘Predictive validity’ is a measure of accuracy used in science and psychology.
It’s used to quantifying how accurately an assessment can predict future behaviour.
At Applied, we use work samples to test candidates.
Work samples take parts of a job and turn them into hypothetical tasks/questions. The idea is to get candidates to perform parts of the job before actually getting the job.
Sure, someone’s work experience or academic background could signal that they’re highly skilled…
But it could also just be signifying their socioeconomic background.
Since material conditions and skills are extremely difficult to tell apart just by looking at a CV, the fairest way to make hiring decisions is to drop CVs completely in favour of more skill-based assessments.
Instead of a CV/cover letter, we ask candidates to anonymously answer 3-5 work sample questions.
Here’s what this looks like within the Applied Platform:
Candidates' answers are then chopped up, randomized and reviewed question-by-question so that no candidate's answer is ever viewed in its entirety to avoid ordering biases.
Ideally, we recommend having three reviewers score answers against set criteria. This will give you more accurate, objective scores to make hiring decisions.
Not only will any individual’s biases be averaged out, but collective judgment is generally more accurate than that of a single person- so long as scoring is done without conferring!
When it comes to interviews, we use a combination of work samples, case studies (candidates are given a larger project to think through) and work simulations/role-playing tasks.
By using data to make decisions, a process like this will enable you to track where your best candidates tend to come from and how various demographics progress through your assessment rounds.
If you notice that candidates from a certain group are dropping off at a certain point, then you’ll be able to see exactly what is causing this. It could be the wording of a question its scoring criteria that's resulting in a disadvantage.
Does this approach work?
If we look at one of the most famous examples of ‘blinding’ from back in the 70s, researchers managed to double the number of women getting through orchestra auditions by anonymizing the auditioning process.
But what about the modern-day workplace?
Well, a quick glance at the data tells us that organizations tend to see a 3-4x increase in the attraction and selection of ethnic minority candidates using Applied
And we also know that over 60% of people hired through our process would’ve been overlooked via a more traditional one, mostly from underrepresented groups.
This also extends to gender diversity, where we tend to see drastic improvements in representation even in the most male-dominated fields.
Is software necessary to improve diversity?
The truth is: you don’t need blind hiring software to improve diversity.
Anonymizing and data-proofing your hiring doesn’t require software. Spending money on blind hiring software will make many parts of the process more efficient and demonstrably fair, but the core practices of our platform can be implemented for free:
- Remove identifying information from applications
- Use work samples to test skills
- Conduct structured interviews
- Score candidates against set criteria
- Have multiple reviewers to mitigate bias
The one feature that will be struggle, however, is tracking diversity metrics.
Although possible using Google Sheets, collecting diversity data and then inputting every score across your hiring process would likely take more time than you have to spend.
Using blind hiring software like Applied, all of this data will be collected automatically to generate reports.
This also means that every candidate receives personalized, objective feedback based on their skills/performance.
Key takeaways: blind hiring software
- Blind hiring software anonymizes applications to reduce bias
- Research shows that this approach generally leads to improvements in diversity
- For the best results, consider moving beyond just anonymization, testing skills instead of using CVs
- Some of the most impactful practices can be implemented without using software, although this will help with automation and diversity tracking/ reporting
Applied is the essential platform for debiased hiring. Purpose-built to make hiring empirical and ethical, our platform uses anonymized applications and skill-based assessments to identify talent that would otherwise have been overlooked.
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