How AI Simplifies the Search for Diverse Talent

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Today, many companies are making diverse hiring a key component of their business strategies — and they are making these efforts more transparent to the public, too. In fact, 50 of the US’s largest public companies have agreed to disclose their Equal Employment Opportunity Commission data on the racial, ethnic, and gender demographics of their workforces.

Despite these good intentions, however, many employers continue to fall short of their diversity goals. Here are some of the challenges organizations face in trying to hire more diverse talent and how artificial intelligence and machine learning technologies can help solve them:

Challenge No. 1: Limited Talent Pools

Most companies use tools like LinkedIn to source talent, but not every candidate is one LinkedIn. Moreover, some of LinkedIn’s paid tiers, like Recruiter Professional Services, still limit users to only seeing talent within three degrees of separation from their networks. That means that you can only view the profiles of people who are connected to you or connected to your contacts. This limits your access to a broader, more diverse talent pool.

This problem can be avoided by making an active effort to tap broader talent pools rather than returning to the same talent sources for every hire. For example, some AI-enabled recruitment tools now allow recruiters to access larger databases curated from hundreds of public sources. This allows recruiters to view many different candidates from a variety of talent pools at once

People leave digital footprints in many places aside from LinkedIn. For example, engineers are more likely to post their professional skills and projects on sites such as GitHub and Stack Overflow. Most of that information is public, but it can be extremely time-consuming to hunt it down manually. These aforementioned AI tools collect that public information for you, so you can access a wider range of talent without spending hours searching every possible platform for candidates.

Challenge No 2: Antiquated Search Methods

For years, recruiters have used keyword searches to look for talent, but this is an inefficient tool for the task. Keyword search, also known as Boolean search, is a trial-and-error-based sourcing method in which recruiters source candidates based on the keywords contained in their profiles. Recruiters must then sift through these candidates manually, likely missing many qualified candidates along the way. Keyword searches can only show you people with the “right” keywords in their profiles, which means highly qualified candidates may not appear in your searches simply because they used different language to describe their work experience.

Keyword searches are particularly ineffective for diversity hiring. According to research we conducted at Talenya, candidates from different backgrounds tend to describe themselves in different terms. For example, white male candidates post 10-17 percent more skills on their profiles and use significantly more text to talk about themselves. As a result, white male candidates rank higher in keyword searches, while candidates from other backgrounds are lower in the results and more likely to be skipped over.

Rather than relying on keyword searches, recruiters can leverage AI-enabled searches for a more granular, curated approach to sourcing. Rather than matching candidates based on single keywords, AI-enabled searches match candidates against the whole job description. From there, some AI sourcing tools will also allow the recruiter to rate the matches they receive. Using machine learning technology, the sourcing tool will then look for commonalities in the recruiter’s selections, using this information to identify the most important factors in candidate fit. Then, the AI can bring in more candidates that are similar to the ones the recruiter approved while eliminating candidates who were similar to the ones that were denied.

Some AI tools can go even further by predicting skills that may be missing from a candidate’s profile and adding them back in, ensuring candidates don’t get overlooked just because they forgot to list a certain skill.

Challenge No. 3: Unconscious Bias

Even with the best intentions, hiring managers may have biases that influence their hiring decisions. A candidate’s picture or name may evoke an unconscious bias in the hiring manager without them even realizing it. Here, AI can help make the process a little more objective.

As we’ve already established, AI can help broaden your talent pool by sourcing talent from various pools based on dozens of different parameters. But it can also eliminate descriptors from candidates’ profiles, allowing recruiters to select talent purely based on their merits. By putting the focus more squarely on candidates’ actual qualifications, AI can help reduce — and potentially eliminate — the influence of human bias in the recruiting process.

Challenge No. 4: Discriminatory Job Requirements

Hiring managers specify the job requirements recruiters use to run talent searches, but these requirements may unintentionally be excluding candidates from certain backgrounds. Education, seniority level, location, and years of experience are some examples of requirements that may limit your search for talent; even something as seemingly benign as a job title can impact the candidates you source.

When a job has several such requirements, it is impractical to create hundreds of variations of each search to diversify your talent pool. However, by using certain AI tools, recruiters can run hundreds of variations of a single search and identify small changes — like tweaking keywords or altering requirements — to expand the talent pool. For example, AI can run a simulated search to tell you whether your required length of previous experience is unintentionally blocking candidates from certain backgrounds. Armed with that knowledge, you can choose to change the required amount of experience for the role. That doesn’t give any particular job seekers preferential treatment, as it would apply to all candidates for the role.

There are many challenges to diversity hiring that prevent companies from reaching their goals. Fortunately, new AI and machine learning technologies are making it easier for recruiters to expand their searches and bring more kinds of talent into their pipelines. Now, companies have a real opportunity to increase diversity in their workforces while giving more candidates a fairer shot at landing the job.

Gal Almog is cofounder and CEO of Talenya.

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Gal Almog is cofounder and CEO of Talenya, a world leader in AI-powered tools for diverse talent sourcing. Its Diversity AI solution increases the participation of diverse talent in the hiring pipeline by more than three times.