Recruitment Bias Report: How bias affects hiring and how to remove it

Joe Caccavale





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Traditional hiring is biased.

No matter how well-intentioned you may be, we’re all subject to unconscious biases that affect our decision-making.

Below, we’ll explore the key studies around recruitment bias and share research-backed strategies for removing this bias.

Here at Applied, we practice what we preach: all of the advice we’ll share below is based on behavioural science and is exactly what we do to ensure our hiring is fair, inclusive and predictive….

In this report:

  • Racial bias in hiring (UK/USA data)
  • Gender bias in hiring (UK/USA data)
  • The impact of ordering effects
  • How to remove bias from your hiring process
  • 1) The danger of CVs
  • 2) Fairer interviewing
  • 3) A word on culture fit

Racial bias in hiring


Although not the most recent, the biggest study of racial bias in hiring here in the UK is this 2009 report.

Applications were randomly assigned an ethnicity and sent off to a range of employers - controlling for everything except the candidates’ ethnicity.

Each ethnicity was tested against white candidates to mitigate any variation across different employers (this is why you can see varying success rates for white candidates in the chart below).

Recruitment bias by ethnic group

The result: white candidates were favoured in around 47% of the tests.

And Indian candidates would have to send twice as many applications to get the same callbacks as white-named applications. 

  • Equal treatment: 35% 
  • BME favoured: 18% 
  • White favoured: 47%

Another, more recent study from the University of Oxford had similar findings around recruitment bias…

After sending out identical applications with only the candidate’s ethnicity being changed, they found that candidates from minority ethnic backgrounds had to send 80% more applications to get the same results as a White-British person.

Number of applications needed for 1 callback by ethnicity

24% of applicants of white British origin received a positive response from employers…

Compared to just 15% of minority ethnic applicants.

And the researchers also found that this bias had barely improved from when they conducted similar experiments back in 1969.

Inside Out UK replicated these correspondence studies on a smaller scale, focussing solely on Muslim-sounding names (which although not technically a ‘race’, is an ethnicity).

Below are the results…

Callbacks: Adam vs Mohamed

100 job opportunities were applied for using identical CVs (the name being the only variable that changed)

  • Adam: offered 12 interviews
  • Mohamed: offered 4 interviews

This means that candidates with a Muslim-sounding name are 3x more likely to be overlooked for a job.


A 2011 study conducted in Canada sent off thousands of randomly manipulated CVs in response to online job postings.

Researchers found significant discrimination across a variety of occupations towards applicants with foreign experience or those with foreign names. 

(We’ve simplified the findings in the chart below, but you can see the full results here).

Callbacks by resume type and ethnicity

The rough baseline callback rate for an all-Candian/English CV was 16%.

Changing only the name to one with Indian origin lowered the callback rate by 4.5% (to 11.5%).

Changing it to one with Pakistani or Chinese origin lowered this slightly more, to 11.0% and 11.3% respectively. 

Overall, CVs with English sounding names are 39% more likely to receive a callback.

The same went for education and experience. The more ‘foreign someone’s background, the lower their chances of getting a callback.

Looking specifically at the effect of Black-sounding names on callbacks, one study involving over 5000 CVs found that applicants with white-sounding names needed to send roughly 10 CVs to get a callback, whilst applicants with African-American names needed to send about 15.

Callbacks: Black vs White sounding names

A study from 2016 tested whether or not employers with pro-diversity language in their job ads showed any less recruitment bias.

Researchers found that when applying to an organisation that presents itself as valuing diversity, minority background candidates were less likely to ‘whiten’ their CV.

However, these organisations’ diversity statements were not actually associated with reduced discrimination.

Here are the results for Asian candidates...

Name whitening vs callbacks (Asian)

And for Black candidates...

Name whitening vs callbacks (Black)

For more studies like these, check out our Racial Discrimination in the Workplace Report

Gender bias in hiring


At a glance, it may seem as gender bias is a thing of the past.

Looking at the results UK study, you can see that at a very high level, men are only slightly ahead of women when it comes to callback rates.

Callback rates by gender

However, if we then look at individual roles, it becomes apparent that there is in fact a measurable bias against women.

Below are the results of a study carried out across the English labour market.

Identical applications were sent to a wide variety of roles, changing only the sex on the application.

Callbacks by role

For ‘typically female’ roles (like a secretary in this case), women are favoured.

And for ‘typically male’ roles, men are favoured.

So gender bias seems to occur when people attempt to step out of the roles they’re stereotypically associated with.

We also know that, according to Mercer’s 2020 report, the higher up the corporate ladder you go, the fewer women you’ll come across.

If women are not who we expected to see in more senior roles, then it’s safe to assume that they’ll be subject to recruitment bias.
Female representation vs seniority


The most famous US study on gender bias in hiring looked at science faculties at universities.

These science faculties were asked to rate candidates' applications to a laboratory manager position - the applications were randomly assigned a sex.

They were rated for competence, hireability, and the likelihood of receiving mentorship.

Science faculty ratings by gender

Despite the applications being identical (bar sex), women were rated as being less competent and hireable than male candidates.

Men were even offered higher starting salaries.

It’s also interesting to note that men and women were equally likely to show recruitment bias against female candidates.

This seems to point to much of this bias being unconscious, rather than an explicit plot to keep women out of science faculty jobs.

Another study delved into recruitment bias in law firms.

Researchers created two fictitious candidates, completely identical apart from their sex and class indicators.

They found that only men perceived as higher class benefitted from this background.

And for women, a higher-class background actually led to a “commitment penalty.” 

After interviewing the employers, the researchers discovered that higher-class women were seen as being less committed to working and more likely to leave after having children.

Callbacks by class and gender

For a more comprehensive look at the research, you can read our Gender Bias in Hiring Report.

Ordering effects also influence hiring decisions

Whilst the evidence clearly shows that certain demographics are disproportionately disadvantaged by traditional hiring processes, there are also ordering effects at play that can affect any candidate.

Even if we were to anonymise applications (something we’ll get onto soon), the context in which we’re presented with information can still influence our decision-making.

One of the most compelling studies on ordering effects is this study of Israeli judges.

Judges parole-granting decisions were analysed over the course of a day.

Below are the results…

Decision fatigue chart

What exactly are we looking at here? Well, we can see that their decisions became harsher over time.

The more tired we become, the more risk-averse we get - a phenomenon known as ‘decision fatigue.’

The spikes we can see in favourable decisions were following the judges’ breaks.

It’s not hard to imagine how this could be applied to recruitment bias.

A tired hirer with a pile of CVs to get through will likely grow harsher as time goes on.

We’re also more likely to remember intense moments and the end of moments of an experience more vividly.

The ‘peaks’ and the end weigh higher in our mental calculations.

These snapshots dominate the actual value of an experience.

This unconscious bias is known as the ‘peak-end effect’.

One study put this to the test - asking participants to take part in two trials which involved putting their hand into uncomfortably cold water.

Trial 1: Place hand in 14° water for 60 seconds.

Trial 2: Place hand in 14° water for 60 seconds and then 15° water for 30 seconds

Peak-end effect experiment

Participants reported that despite being uncomfortable for longer, they found Trial 2 less painful.

Why? Because the end of the experience was less painful (and the peaks were the same)

Coming back to recruitment bias, peaks and end will be spread across multiple candidates, causing an unfair memory of each applicant individually.

Those whose interviews had high points or ended well may be viewed more favourably as a result - despite not necessarily being more suitable.

How to remove recruitment bias - here’s what’s been proven to work

We’re all biased, so we have to design processes that remove it

Unconscious bias affects everyone.

It doesn’t matter how fair or objective you believe yourself to be - unconscious bias is simply a part of being human.

When we look at the sort of outcomes the studies above result in, there are likely a number of biases causing this.

Perception bias: we believe something is typical of a particular group of people based on cultural stereotypes or assumptions.

Affinity bias: we feel as though we have a natural connection with people who are similar to us.

Halo effect: we project positive qualities onto people without actually knowing them.

Confirmation bias: we look to confirm our own opinions and pre-existing ideas about a particular group of people.

Attribution bias: we often attribute others’ behaviour to internal characteristics and our own behaviour to our environment - we are more forgiving of ourselves than others.

For the most part, we don’t know we’re falling prey to one of these biases (hence ‘unconscious’).

Unconscious bias tends to occur when we use the fast, shortcut-based part of the brain to make decisions that should be made using the slower, more conscious part.

This unconscious defaulting happens without us being aware of it.

What some hirers may refer to as their ‘gut instinct’ is essentially just unconscious bias - it’s using quick-fire associations and mental shortcuts to make snap judgments. 

A candidate went to Oxford University? That means they must be intelligent.

This bias doesn’t make us bad people but left unchecked, it leads to diversity gaps widening and poor hiring decisions.

Because of the unconscious nature of this bias, training alone doesn’t work.

Humans are biased, and no amount of training will change that.

What does work, however, is carefully designed processes.

You can’t be-bias a person but you can de-bias the process they use to make decisions.

And this is exactly how we remove recruitment bias here at Applied.

Get rid of CVs and start testing for skills upfront

If the research covered in this report has taught us anything, it’s that CVs have to go.

The more we know about a candidate, the more grounds for bias we have.

A candidate’s name, address, date of birth can all trigger biases that lead to them being unfairly favoured or overlooked.

So, first things first, anonymous applications are a must.
CVs and recruitment bias

You’ll notice that on the imagine above, the candidate’s education and experience have been flagged as grounds for bias.

Although education and experience are they key factors for most hirers, they’re not actually predictive of real-life ability.

When scanning a CV, a big-name employer or prestigious university may jump out at you, but when we look at the research into their predictive power, education and experience don’t tell us very much at all.

Predictivity of hiring methods chart

You can read the metastudy the chart above was based on here.

You’ll notice that the most predictive form of assessments are ‘work sample tests.’

This is what we use to screen candidates here at Applied. 

We’ve found that organisations using our process hire up 4x more candidates from minority backgrounds.

And 60% of the people hired would’ve been missed via a traditional CV screening.

SO, WHAT ARE WORK SAMPLES? Work samples take parts of the role and simulate them as closely as possible by asking candidates to either perform a task or explain how they’d go about doing so. They’re similar to your typical ‘situational question’ except they pose scenarios hypothetically - focussing on potential over experience.

Work samples rank so highly in terms of predictivity since they essentially get candidates to think as if they were already in the role.

Education and experience aren’t completely useless, but they’re proxies for skills.

Work samples, on the other hand, test for skills directly.

They’re created by taking the skills required for the role, and then thinking of a scenario or task that would test this skill, should the candidate get the job.

Instead of asking candidates if they’ve done something before, work samples simply ask them what they would do.

Here’s an example we recently used to hire a Sales Development Representative:

It's been a busy week and it's now late on Friday afternoon. You have 4 things you've yet to get to this week, but only time to complete two. Imagine for some reason working late/over the weekend is impossible. Which 2 would you do and why? What would you do regarding the others?

There aren’t right or wrong answers. We're just interested in how you think about priorities. 

1. There is a Webinar next week, we typically go for 200 sign-ups but it only has 100 so far. Perhaps an extra campaign? 

2. Your line manager has asked for a report on how the most recent campaigns have been going as he is chatting with the CEO later that evening.

3. The Community Lead is keen to launch a campaign for next months Training Days first thing on Monday. You haven't written the emails, or got the list of new targets yet.

4. After the last training days you haven't followed up with the attendees to see if they would like to demo the platform. There is about 80 to contact.

Skills tested: Communication, Prioritisation 

Instead of a CV/cover letter, we have candidates submit 3-5 of these work samples anonymously.

Once submitted, we then put the answers through a mini-process to ensure things like ordering effects and groupthink are removed from decision-making. 

Chunking: applications are sliced up so that you’re comparing candidates’ answers question by question, as opposed to reviewing answers candidate by candidate (one good/bad answer can muddy our perception of the others). 

Randomisation: for each question, the order in which answers are reviewed is randomised to avoid ordering biases (applications viewed first tend to be scored higher).

Review: We have three team members score answers against set criteria. This uses the power of crowd wisdom - the rule that collective assessment is more accurate than that of a single person. Answers are scored independently, without conferring.

Work sample cheatsheet

Data-proof your interviewing

When it comes to interviewing, recruitment bias can be hard to avoid.

However, there are steps you can take to make interviewing more fair and objective.

The first change you’ll want to make is switching to structured interviews 

WHAT ARE STRUCTURED INTERVIEWS? Structured interviews entail asking all candidates the same question in the same order - this is to make your interviews to be as uniform as possible.

We recommend using work sample-style questions at the interview stage too.

So where you might usually ask candidates about a time when they did something or showed a certain skills, try asking them how they would tackle that thing.

Hiring is ultimately about assessing potential.

You want to see what people can do, not what they have done.

Sometimes past experience does prepare someone for doing that thing in future, but we’d rather test this directly rather assuming that someone is capable just because they’ve done something before.

Interviews are your chance to see how candidates would think and problem-solve if they were on the job.

Your interview questions can be a mix of hypothetical questions, role-playing tasks and even mini-case studies…

Give candidates a real-life data-set to talk through or a project that needs planning and see how they’d approach it.

Here’s an interview question we asked candidates for the same Sales Development Representative role above:

Describe how you would go about finding and contacting 500  Heads of Talent to get them to come to our Training Days. You have a week

Skills tested: Research, Communication, Prioritisation

This task is something candidates would be doing if they got the job.

Whilst there are no right answers, we wanted to see how they’d approach this task and the process they’d come up with.

Scoring criteria is absolutely essential for removing recruitment bias.

For every work sample and interview question, you’ll need to give yourself a scale and criteria against which to score answers.

We recommend a simple 1-5 star scale - writing a few bullet points detailing what a good, mediocre and bad answer would include.

Here’s an example review guide for the interview question we looked at above:

1 Star 

- No real effort made

3 Star 

- Mentions multiple follow-ups and comes up with a credible sourcing strategy

5 Star 

- A clear multi-touchpoint process that crosses social media and email.

- Mentions specific technologies with a view to automation and continuous improvement. Where they don't mention a specific one - clear about what it's supposed to do (and it exists)

- Talks about ways in which we can add value to each contact

For each interview round, we have three interviewers scoring candidates - without discussing with each other.

Scores can be written down during the interview or immediately after.

Ideally, you’d have two new members for each round, but this depends on your team size/capacity.

As a general rule, the more diverse your panel, the more unbiased the scores.

Testing for culture fit without bias

Culture fit has the potential to be problematic when it comes to de-biasing your hiring process.

If your organisation sees culture as something fixed, that people have to adhere to, then your culture fit testing is likely to be biased.

To avoid all the pitfalls associated with culture fit, you could switch to ‘culture add’ - an assessment of what people can add to your culture.

But we’d recommend side-stepping culture altogether.

At Applied, we test for mission and values alignment.

What we’re testing for is: are candidates as passionate about de-biasing hiring as we are and are they aligned with our team’s values?

For any role at Applied, we ask this question:

Why Applied? Why now? Why a start-up?

Key takeaways:

  • Swap CVs for work samples
  • Use structured interviews 
  • Give yourself scoring criteria
  • Test for mission and values alignment over culture

Applied was designed to make objective, unbiased hiring easy. Find out how we’re changing the way the world hires by browsing our resources library or booking a demo to see how it works for yourself.