AI and the Search for a More Diverse Workforce

Any executive or hiring professional, particularly in tech, knows that recruiting and retaining diverse talent is one of the biggest challenges in the industry.

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Competition is fierce, and the pool for top candidates is seemingly small. That means finding, recruiting, and retaining talented employees is more challenging than ever before. By hiring and retaining a diverse workforce, businesses experience many benefits, such as increased organization performance, an influx of innovative perspectives, and high diversification of the labor market. In fact, in one U.K. study, researchers discovered that greater gender diversity on the executive team could positively affect performance. Furthermore, for every 10% increase in a diverse workforce that enterprises executed, profits increased by 3.5%, a notable jump.

But even armed with these statistics and the best of intentions to make good on diversity-minded goals, businesses often struggle to actually execute. They often lack the capacity and proper resources to do anything about removing barriers to equality in hiring and retention. In other words, they have not been supplied with an efficient technological method of ensuring diversity in their talent acquisition practices.

Artificial intelligence (AI) is opening up new opportunities to help enterprises of all sizes make more informed decisions and unlocks the potential to make a significant impact in creating large-scale change in the tech industry’s Diversity & Inclusion initiative. Diversity doesn’t just refer to race; talented employees have also missed opportunities because of disabilities, ageism, gender, sexual orientation, and other protected criteria because of a flawed hiring system.

How can AI play a role in recruiting diverse candidates? Research shows that both conscious and subconscious biases play an enormous role when it comes to hiring and recruitment operations, and sometimes many of these biases simply stem from an utter lack of data. A Harvard study from 2017 notes that poorly written job descriptions are a deterrent to female applicants because they usually don’t apply unless they are a very good fit for the job. Data from our platform, in addition, have also revealed that female candidates with the same capabilities fare 10% worse than the male candidates in the initial filtering process. Even worse—female candidates with the same capabilities fare 35% worse than the male candidates during in-person interviews. In case of a tie, the preference often leans toward male candidates. Due to these subconscious biases, it is highly likely that recruiters are failing to identify the best talent, which can result in the elimination of female candidates and people of color without even being aware of it. Ultimately, this leaves the business falling short—despite best intentions—of building a diverse workforce.

To aid companies’ intent on overcoming bias at work, we have made sure that our platform has the ability to mask anything in a candidate’s profile that would reveal their background—name, picture, college (if applicable), etc. This forces the hiring managers to focus on capabilities and future potential, not on gender or background.

Purpose-built models for people and jobs give millions of applicants much higher probability of landing the “best fit job” and simultaneously gives employers an opportunity to find the “best fit talent” quickly, including those who may not have been previously considered. In addition, casting a wider net, coupled with a matching engine driven by machine learning—can improve person-job match and assist in overcoming implicit and explicit prejudice in the workplace.

While hiring talent, AI can find those coveted purple squirrels and unicorns within the candidate pool by both predicting the most qualified applicant and showcasing someone who might also meet the company’s diversity preferences. Talent acquisition teams using AI—such as those at AdRoll Group and DigitalOcean—are able to see which applicants are the most advantageous fit for available positions within the company by correlating career aspirations to capabilities. AI emphasizes applicants with the best fit, creating the likelihood of a strong match and future productivity and success in the position.

When it comes to diversity-based hiring, companies cannot solely make the hire to fill the objective of having a diverse workforce, although AI can help track those important initiatives to learn how you’re doing and make sure you’re on track. But the objective should not only be to hire diverse applicants; instead, companies should focus on how to hire and then retain these applicants, provide them with internal mobility, and make their work life both fulfilling and inviting. CEOs can now build more diverse, open, and inclusive organizations by having their Hiring professionals and HR executives use the power of AI to transparently transform every level and every group across the organization for better business outcomes.

At current rate, one out of every five employees is likely to leave his or her current employer at any given time. We believe companies should have succession planning for every employee. This is not feasible if done manually. But AI can solve it at scale. Every hiring opportunity—whether the candidate is coming from inside or outside the company—is an opportunity to make the company more diverse. Now you are moving quickly toward building a more diverse and more productive company—and a company that walks the talk.

Ashutosh Garg is the CEO and co-founder of, who is using AI to, in part, address the talent gap and issues in diversity. 

The post AI and the Search for a More Diverse Workforce appeared first on HR Daily Advisor.

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