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How does artificial intelligence drive diversity in recruiting?

Artificial intelligence (AI) is a hot topic in recruiting. While AI primarily aims to automate and increase efficiency of specific stages in the recruiting process, it also contributes to maximising diversity. When used wisely, AI helps avoid biases that sometimes occur when hiring candidates (recruiting identical profiles, focusing on a single criterion, etc.). AI frees recruiters from unconscious biases and helps them foster efficiency and heterogeneity in their hiring practices.

Why leverage artificial intelligence in your recruiting process?

Artificial intelligence (AI) has been around for a while now in the world of recruiting. In many ways, it improves the day-to-day work of recruiters who are constantly on a quest for efficiency and productivity.

Better-performing recruitment processes

AI often makes up the main technology in a number of solutions recruiters use. It makes recruiters’ lives easier by helping them:

  • Sort through large numbers of resumes and/or profiles in candidate pools;
  • Identify the most relevant profiles with regard to a vacancy, through matching.

Screening applications beyond resumes

For a long time, manual resume reading used to be the first step in selecting candidates. Today, with the advent of AI and machine learning, hundreds of applications can be analysed in a matter of seconds, and relevant information sent to recruiters. This doesn’t mean AI necessarily has the last say – recruiters are still the ones who make the final decision, but AI-based suggestions certainly save them precious time.

Need to rapidly identify the best profiles among applications received? Explore the module

One major advantage of AI in terms of diversity is that no resume is overlooked for want of time. Each candidate sets out with the same chances of being considered in the screening stage.

What is machine learning? Machine learning is a subdivision of artificial intelligence that helps increase the precision of algorithms as they continuously learn from data they’ve already processed.

Artificial intelligence to avoid bias and foster diversity

Minimising risks of bias in the recruiting process

Bias (mostly unconscious) can occur at every step of the recruiting process, but it can be particularly troublesome in the early stages of the process, i.e. during candidate sourcing and application screening.

The risk of overlooking some profiles at the screening stage is higher. Not only is there a risk you might miss out on a rare gem of a candidate for an ongoing hire, but you’ll also fail to add them to your talent pool.

Doing away with subjectivity, whether with regard to training, qualifications, sex, age, industry, etc., is perfect for rethinking your recruiting process, especially given the current candidate shortage-ridden context.

Increasing talent diversity in candidate pools

Many recruiters admit that when they screen applications, they tend to focus on specific types of profiles – ones that are often similar to those they’re already familiar with (based on colleagues, partners, former candidates, etc.).

As a result, candidate profiles that are more unconventional compared to those of the company’s current employees have less chance of being selected and therefore of introducing more much-needed skills diversity, in terms of both hard and soft skills.

Such varied and differentiated competencies will be spotlighted and probably positively assessed during the interview stage. So that’s one more reason not to close the door on some talent profiles as early on as the screening stage.

Need to rapidly identify the best profiles among applications received? Explore the module

How to better leverage artificial intelligence to foster diversity in your recruiting?

Providing AI with an accurate description of the ideal candidate

What constitutes a good candidate? Well, it’s hard to say. It depends on the company, on the role, on the industry, on objectives, etc., which is why performance indicators and selection criteria need to be determined by the recruiter and future manager before the process begins.

Artificial intelligence will only be useful if it is fed enough information to analyse and sort through the candidate profiles. The efficiency of your AI depends on the quality of the data it receives to work with

Drafting diversity-centred job vacancies

In order to secure accurate matches between applications and job vacancies, and to enable diversity among the AI-selected profiles, job vacancies need to be drafted in accordance with a specific methodology.

Because biases can arise as early as the vacancy drafting stage.

In the job vacancy, avoid including information such as:

  • A specific nationality
  • The need to speak a specific language to mother-tongue standard
  • A specific educational institution
  • A role designation that is not neutral nor inclusive in terms of sex
  • A semantic field that overemphasises experience (“senior”, “experienced”, etc.)

Focusing on soft skills to encourage diversity

According to Badr Boussabat, author and regular panellist on the subject of IA, and President of the NGO “AI TOGETHER”, “Artificial intelligence increases our responsibility as human beings in the recruiting process because it generates even more information thanks to collected data […]. Thanks to AI, recruiters are in a position to further explore relevant points raised during the interview stage. In other words, AI gives recruiters more time to spend analysing soft skills.

Artificial intelligence allows recruiters to fully embrace their human role and discover more diverse profiles during interviews, which they’ll then be able to analyse before deciding which candidates to move on to the next stage.

Identifying the best profiles based on soft skills can also be achieved using other forms of technology, including video recruiting.

Need to identify candidate soft skills easily? Explore the module

Going the extra mile with predictive hiring

Artificial intelligence can sometimes be supplemented with predictive hiring technology.

Predictive analytics uses historical data to predict future trends. Recruiting tools can leverage predictive analytics to determine the probability that a candidate will perform in a role, based on their experience, skills, and interests. Other aspects can be analysed to reveal the probability that a candidate will be a good fit with the company’s culture.

This can be achieved by having candidates undertake various tests, including aptitude tests, personality tests and competency assessments.

Such tests can be introduced once your diversity-driven, AI-enabled recruiting strategy has successfully been implemented.

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