There’s a huge need for diversity. For one thing, customers are more diverse than ever. So to understand and serve them better, companies need to be more diverse. Same with employees. This applies to contractors, contingent workers, boards, suppliers, and so on.

That continues to be a challenge. People are inherently good, but we’re all biased: our histories and experiences can create subtle, unconscious biases which can lead to non-objective decisions, especially when it comes to hiring.

With that in mind, AI can mask people’s identities like gender or race, hiding their name, and their photo.

AI can go beyond that, such as hiding that someone went to a certain college that’s mostly female, or hiding that they played field hockey or synchronized swimming, sports that are mostly female. Instead, AI could just say that someone got four varsity letters or was a captain.

AI and talent intelligence platforms need to achieve the highest equal opportunity standards for achieving diversity. They need to be regularly updated and monitored, so that when new features are added you are guarding against bias.

Talent Intelligence Platforms can also provide analytics. Where are people dropping off in the hiring cycle? Are under-represented groups not being interviewed, or dropping off at the offer stage?

Many people don’t self-identify their race or gender. But AI can infer that information across groups of people, and help companies build those analytics. In about 90 percent of cases, AI knows from the profile it has built from hundreds of public data sources whether someone belongs to a group a company is targeting for recruitment, such as military veterans.

The great thing is that an AI-driven talent intelligence platform can help companies make adjustments to be more diversity-friendly. If a company’s analytics say that it’s eliminating too many female candidates, it could adjust the parameters it has set to create a more diverse set of candidates. The AI shows companies using a Talent Intelligence Platform the point in the process, such as the screening, interview, or offer stage, where a lack of diversity is a problem. A better understanding of diversity metrics throughout the process helps achieve diversity goals.