Behavioural Science and Hiring: Inclusive Attraction & Assessment

Pam Dang • 8 min read

Diversity and inclusion in the workplace has been a primary area of research in the last decade, given its significant impact on boosting revenue and improving decisions and problem solving. Organisations have learned over the years that the process of promoting and fostering an inclusive workplace culture can be broken down into many stages and different elements, all starting with recruitment and hiring.

Using insights from behavioural science, we summarise some key findings that are used to ensure inclusivity in two early phases of recruitment: inclusive attraction and inclusive assessment.

“Men apply for a job when they meet only 60% of the qualifications, women won’t apply unless they meet 100% of them.”

Inclusive Attraction

To attain an inclusive workforce, companies must diversify the talent pool – they need to attract a fair share of talent and job seekers from diverse backgrounds at the get-go.

Studies have identified several factors that influence a company’s attraction of an inclusive pool of applicants, ranging from job adverts (e.g. job description) to the company’s  brand messaging or organisation website. For many applicants, a job advertisement is their first impression of and introduction to a company, yet only 13% of job ads contain inclusive language as reported in March 2020. 

So what are some features of a job advert that can discourage applicants from underrepresented backgrounds to apply? First is the wording or the tone being used. Adjectives used in job descriptions often convey an unconscious gender bias, including words such as “competitive” or “leader” that are often associated with masculine stereotypes, and can be a turnoff for female job seekers. The use of this ‘gendered’ language to describe wanted qualities is especially dangerous in gender-imbalanced sectors like STEM

In addition, the format of job adverts (such as lengthy job descriptions with too many bullet points on the position’s responsibilities) can also subliminally discourage female job seekers from applying. On the other hand, messages that underline the company's collaborative nature can spike a significant interest from women and other underrepresented groups, especially in male-dominated fields like computer science. Similarly, adverts that highlight a company’s values and missions are also perceived as more appealing for female job seekers.

These findings have been around for a few years, so what are some solutions to overcome these mistakes and ensure inclusive attraction of applicants?

“Solution: make it easy for diverse candidates to apply.”

Many businesses have made significant efforts to include gender-neutral language in their recruiting materials. Textio, a leading augmented writing platform, uses artificial intelligence (AI) to offer automated text tools that detect and analyse gendered language in job adverts, helping companies attract a greater level of applicant diversity.

Impactually, attempting to receive more female applicants in STEM, offers other solutions such as widening recruitment platforms, using words that appeal to female applicants or separating job specifications into 2 lists of ‘musts’. Similar nudges are also used in recruitment strategies to engage more female talent in the legal sector

Norvatis, a healthcare company, takes an integrative approach and combines diversity training with nudges, using AI assessment tools to scan for gendered language in their job ads. 

A study by BIT and Indeed found that a double-nudge on job flexibility (i.e., by prompting choice about flexible working on job adverts) can increase job applications by up to 30% and encourage job seekers to apply, especially women, given that they are twice as likely to work flexibly


Inclusive Assessment

There are at least 13 common recruitment biases that recruiters are often unaware of, which affect their judgment and assessment of candidates, resulting in biased hiring decisions.

Halo effect (one positive aspect in a candidate influences our whole judgement of them – “he went to this great school!”) and affinity bias (we like people who are similar to us – “she plays volleyball and so do I!”) are just two examples."

Traditionally, many organisations often invest significant amounts of time and resources into diversity training programs. However, the impact of these (often costly) programmes are rarely measured, can backfire, or do not last in the long run. Or consider that many tech companies and law firms are adopting the Rooney rule. A term originating from American football, this rule requires a team to interview at least one person from an ethnic minority for leadership positions. However, this often shows ineffective changes in diversity or inclusion.

There are simple tweaks that can be made to use [key selection and assessment tools] in a more effective way [and reduce] biases and judgement errors that may occur on the assessor’s side when using these tools."

To remove bias in candidate review, recruitment platform Applied practice the following:

1. Substitutes a set of prompts or questions of work-related tasks for traditional CV screening.

2. Requires up to three recruiters to review candidate responses in a blind-evaluation process.

3. Uses review algorithms to ensure responses are randomised within each question and across all recruiters. 

AI or digital platforms (like Oleeo or Eligo IQ) use machine learning algorithms to make prescriptive recommendations of candidates’ abilities and experiences from their CVs and application questions, albeit there are unintentional outcomes and possible biases within these AI tools themselves. 

Going one step further, Unilever uses predictive hiring games on the Pymetrics platform, based on neuroscience and AI, to assess applicants’ soft skills. To tackle the concern that these strategies only delay discrimination until the interview stage, Unilever uses an AI-enhanced video-interviewing platform, HireVue, to gather data on candidates’ answers, voices, and body language to predict job performance outcomes. A voice-changing software,, is also a solution for biased interviewing processes. 

Finally, several companies are using virtual reality (VR) to assess how candidates would approach situations on the job, which can reduce bias by focusing on measurable skills and traits. 

To conclude, behavioural science has been used more widely in designing strategies and tools to improve recruitment inclusivity and has shown positive feedback. Nevertheless, companies must beware that inclusivity in early hiring stages does not guarantee a diverse and inclusive work culture. Continuous testing and learning, as always, are key to maintaining and improving these outcomes. 

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