Inclusive Hiring: 4 Steps to an Ethical, Data-Driven Recruitment Process

How can you make sure that every candidate that applies to your organization gets a fair shot?

Whilst there’s no shortage of equal opportunities statements lurking at the bottom of job ads, this doesn’t mean that their hiring is any less biased than the next.

Fair, inclusive hiring is what we live and breathe here at Applied HQ - we’ve drawn on mountains of research as well as our own trial and error to refine the process below.

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1. Write inclusive job descriptions 

The words you use in your job descriptions will have a direct, measurable impact on who applies.

If you want to build an inclusive hiring process, your first need to make sure you’re optimizing for diversity right at the top of the funnel.

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Use gender-neutral language

Women will be put off applying if your job descriptions contains an excessive amount of masculine-coded language.

Openreach's research found that women were 50% less likely to consider roles that have a coded gender bias.

Why? Because words carry subconscious meaning and so masculine-coded terms imply (albeit subconsciously) that a man would be a better fit.

By simply removing gendered language altogether, you can attract a more even gender split.

Openreach’s study also found that although 80% of women said they wouldn't consider working in engineering, 56% became interested once the word ‘engineer’ had been removed.

We tested the effects of gender coding using our own job descriptions too…

By simply removing certain words and phrases, we were able to boost female applications.

Psst… We built our Job Description Analysis Tool to detect and suggest alternatives to gendered language - so that you can prove your job descriptions are inclusive and conversion-optimized.

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Don’t list too many requirements

According to LinkedIn's data, women are 16% less likely to apply for a job after viewing it and overall, apply to 20% fewer jobs than men.

Why is this?

Well, research has shown that typically, women won’t apply for a role unless they meet 100% of the criteria, whilst men will apply when they meet just 60.

This is likely due to the fact that women are more socialised to follow the rules.

If we look at the results of the survey below, the most common reason for not applying (for both genders) was “I didn’t think they would hire me since I didn’t meet the qualifications, and I didn’t want to waste my time and energy.”It’s not that they thought they wouldn't be able to do the job...

They didn't think they would be hired at all if they didn't meet the requirements!

So, instead of listing a wishlist of education and experience requirements, we’d recommend listing 6-10 core skills and working characteristics that are needed to do the job.

Read our full guide to crafting inclusive job descriptions here.

Key takeaway: make sure your job descriptions are to-the-point and free of masculine-coded language (either neutral or feminine-coded will improve gender diversity).

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2. Anonymize your screening process

Truly inclusive hiring should guarantee that unconscious bias doesn’t affect hiring decisions.

And the only way to do this is by anonymizing applications during the applicant screening process. 

Why? Because no matter how well-intentioned we are, humans are prone to bias.

Remove identifying information from CVs/resumes 

We humans have a tendency to make snap associations and resist the unfamiliar. 

Whilst in our day-to-day lives this can actually help us get things done (we don’t have time to consider every micro-decision our day demands), these tendencies lead to candidates from minority backgrounds being disproportionately overlooked when it comes to hiring.

Candidates with a non-White-sounding name have to send 70% more resumes to get the same number of callbacks as their White counterparts.

This is why anonymization is crucial to designing a genuinely inclusive hiring process.

The research is clear - unconscious bias training doesn’t work.

Since most bias is unconscious, simply being made aware of it doesn’t result in more objective decision-making.

Inclusive hiring requires a transformation of the process itself, rather than the people in it.

De-biasing a person doesn’t seem to be possible (at least not cost-effectively) but simply removing the information that leads to bias does result in fairer outcomes.

What to remove from a CV:

• Name
• Date of birth
• Address
• Hobbies
  

Key takeaway: remove identifying information from CVs to avoid bias

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3. De-emphasize education and experience 

Once you’ve anonymized someone’s CV, what are you left with? Their work experience and their academic background.

Now… what if we told you that these too fail to tell us much about someone’s ability?

This may sound controversial, but the data doesn’t lie: education and years of experience aren’t very predictive of ability.

There’s also the fact that people from minority backgrounds find it harder to gain work experience and are less likely to have attended top universities.

Not only does systemic inequality mean that not everyone has access to the same academic opportunities, but the research tells that even when qualifications are identical, these people are at a disadvantage.

This is why here at Applied, we decided to stop using CVs and test directly for skills...

Use data to make decisions, not gut instinct 

So, if you’re not going to use candidates’ backgrounds to make decisions, what are you going to use?

If you revisit the chart above, you’ll see that the most predictive form of assessment is known as a ‘work sample test’.

This is how we screen candidates at Applied - and we’ve found that 60% of people hired via the process would’ve been missed via a traditional CV screening.

Work samples are interview-style questions designed to test the specific skills required for the job.

They take a realistic task or scenario that candidates would encounter in the role and ask candidates to either perform the task or explain how they would go about doing so.

Instead of candidates talking through their achievements or why they’d be a great hire, work samples directly test the relevant skills.

Candidates aren’t required to talk about their skills, they’re instead asked to demonstrate their skills.

Why do work samples make for more inclusive hiring? Because not only do they offer every candidate a fair chance to show what they can do but they also take things like confidence and exaggeration out of the equation.

Here is an example for a Sales Development Representative Role:

Question: We run free training days in order to help Talent professionals de-bias their recruitment processes and understand how behavioural science impacts diversity & inclusion.

Once people understand the science, the chances of them becoming a customer are pretty good! *We provide candidates with a link for more information about our training days.

You have built a list of 1000 Heads of Talent in the US. Write an email explaining who Applied are, and inviting them along. Remember at this stage we aren't selling them the platform, just trying to get them to come along to the training day.

Skills tested: Research, Communication

When it comes to reviewing the answers to work samples, you’ll want to make sure each question has its own scoring criteria.

The closer you can tie decisions to something objective, the more fair and inclusive your hiring will be.

We’d recommend using a 1-5 star scale - adding a few bullet points to give reviewers a sense of what a good, bad and mediocre answer might include.

How to create work samples:

• Decide on 6-8 skills needed for the job
• Think on scenarios of tasks that would test these skills
• Pose these hypothetically
• Give yourself criteria to score against
  

Key takeaway: swap CVs for work samples to test skills upfront

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4. Structure your interviews 

Structured interviews are when all candidates are asked the same, pre-set questions in the same order.

This ensures that each interview is similar enough to compare objectively.

If you want your hiring to be inclusive, you’ll need to make sure that the questions that you’re asking are also optimized for fairness.

The first step towards better questions is forgetting about background. 

You want to know what candidates can do, not where they’ve been.

At Applied, we use a combination of the following to assess candidates:

Work samples: hypothetical scenarios or tasks.

Case studies: similar to work samples - you lay out the context of a bigger project and then ask a series of follow yup questions (e.g. how would you approach this project? What extra data would you need? How would you measure its success?).

Role playing tasks: if there are parts of the job that can be simulated, then ask candidates to role play them. This could be anything from a presentation to a client meeting/call.

Even with all of the above measures in place, an individual interviewer’s biases could still unfairly disadvantage a candidate.

To mitigate this bias, have three interviewer per round.

If each interviewer scores candidates question-by-question, against set criteria, then any individual biases will be averaged out.

The more detailed your review guide is, the easier it will be to get members of different teams to help out with interviewing.

Key takeaway: ask all candidates the same, skill-based questions instead of probing into their background.

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The bottom line

Inclusive hiring isn’t just about valuing different perspectives.

It’s about focussing solely on skills, so that the best person gets the job, no matter how they came to acquire their skills.

As long as we look to background information to hire people, inequality will not improve.

The Applied Team come from a variety of backgrounds, some conventional and some very much unconventional.

Candidates shouldn’t have to hide who they are in order to land a job, we know - from decades or research - that human decision-making is fair from objective if left unchecked.

We’d never have been able to even interview people from such a diverse array of backgrounds if we’d hired them using a traditional process.

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Applied is the essential recruitment platform for debiased hiring. Purpose-built to make hiring empirical and ethical, our platform uses anonymised applications and skill-based assessments to identify talent that would otherwise have been overlooked.

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