Using AI to Make Workplaces More Equitable

Inequity can exist at every stage in someone’s career, from recruitment to progression opportunities. Businesses have a responsibility to remove barriers to equity, however, sometimes it can be difficult to do this when we are all subject to bias, whether it is conscious or subconscious. This is where talent intelligence can help.

Talent intelligence uses deep learning (or AI-based) algorithms to define roles and use internal company data as well as external, local and global data to help decision-makers optimize talent decisions.

Talent intelligence dynamically self-learns until it fully understands the availability, maturity, relevance, learnability and evolution of skills within specific organizations and the larger market. Its resulting analyses provide leaders with complete visibility into the skills of their existing workforce and the training and hiring required to keep pace with industry developments.

It also prevents the type of closed, limited thinking that limits an organization’s potential to attain its equity, diversity and inclusion (ED&I) objectives. With the right talent intelligence platform, ED&I strategies can take on a central role in any organization’s current and future direction.

Equitable recruitment with the help of talent intelligence platforms

As you build out a recruiting strategy, creating candidate lists without reference to factors like age, gender, ethnicity, veteran status and disability status is one must. The best way to do this is by candidate masking.

Candidate masking involves blocking unconscious bias by removing all potential pieces of bias, like those listed, from applications so that only objective data points remain. Having a talent intelligence platform that implements masking and only relies on capability matching, means companies can move away from hiring quotas and don’t base decisions on potential biasing factors. Additionally, generating candidate lists based solely on skills can also help with hiring compliance requirements in specific regions and nations.

Similarly, Equal Opportunity Algorithms identify unwanted trends in source data to deliver less biased predictions, explain how the algorithm arrived at the predictions and illustrate to decision-makers how predictions are independent of potentially biased source data. For example, if most scientists in a company are men, and most of the applications are from men, some people might subconsciously believe that being a man makes someone a better scientist. Equal Opportunity Algorithms make sure that candidate recommendations do not consider gender as a qualification and therefore remove gender bias from the recruitment process.

Not only does this technology sharply reduce hiring bias, but it also increases inclusiveness after an employee is hired because everyone comes in on a level playing field based on their skills and capabilities rather than their pedigree. All employees have the same shot at promotions and networking and mentorship opportunities.

Leverage ED&I analytics to bridge the gap between practice and policy

ED&I analytics finds biases in hiring and also measures the impact of equity policies. These programs show the hiring funnel for each stage and for each diversity category, detecting statistically significant biases. Let’s dig a bit deeper. Suppose, for example, that 10 per cent of all applicants are members of an underrepresented group. This suggests that 10 per cent of all hires should also be members of this group, but that’s approximate. If nine per cent of hires are members of this group, that might be due to chance. If it’s only five per cent, however, perhaps there is a problem, and an analytics program would flag that five per cent is very different from 10 per cent. In other words, ED&I analytics tells you if your hiring outcome is different from what would likely occur due to random chance. It’s then up to you to investigate where and why this discrepancy is occurring. Maybe there is a step in the hiring process that turns away a specific group of candidates. For instance, an assessment process may not be accessible enough and therefore adversely impacts candidates living with disabilities. In many cases, unfortunately, the cause is a human making biased decisions. Hopefully, this person just needs awareness and training, but you may need to remove them from a hiring role.

The other side of ED&I analytics involves equity policies. We gave the example of a company that has 10 per cent of applicants from an underrepresented group and assumed that means approximately 10 per cent of hires will identify as members of this group – if the hiring process is unbiased. But what if the broader goal of preventing bias demands a different target? Perhaps this group represents 20 per cent of the community, and individuals identifying with this group have faced historical disadvantages in applying for a job at the company. Isn’t it only fair that the company makes 20 per cent of hires from members of this group? In this situation, the company may pursue an equity policy to actively increase the share of applications from this group.

Using ED&I analytics, you can find and remove biases that still exist, and lay the foundation for greater fairness in your organization.

Utilizing self-service talent management to create equitable and inclusive progression paths

Talent management services can also include several components for existing staff, such as upskilling and mobility opportunities. Regardless of how you structure talent management, placing a talent intelligence platform behind your talent management services will help create more equitable progression.

With a skills-based approach to talent management, the standard for career advancement becomes what each employee can do, not who they are. Self-service for talent management allows each employee to explore and make career moves on their own time, without the pressure of a performance review or an HR meeting. Both skills and self-service create a transparent career experience. Every employee finds the available options and knows that the same options are available to others. Each employee sees how the organization will make decisions. When employees can understand how a decision such as a promotion is made, they gain confidence in the process.

Finally, your platform should be equally accessible to all employees. Employees with social advantages can’t work around it. Mentors can’t choose who to coach based on their biases. Important projects can’t be staffed at happy hour or by people in the office versus those who work remotely.

With the right technology supporting ED&I throughout the employee lifecycle, your organization can change its entire mindset around careers. Employees will see the presence of opportunity and fair outcomes in action. In time, the expectation of facing bias will be replaced by an expectation of full participation. When this change is achieved, the result will be real equity and inclusion.