No matter how objective we think we are when hiring, we're all subject to human nature and human assumptions. Hiring bias can be as serious as rejecting (or picking) someone because they're a particular race or gender, but it can also be something as innocuous as picking someone for an interview because they went to the same high school you did. These tiny calculations often happen in our heads without us realizing what's going on, and before you know it, diversity bias has crept into the process.
So how can you solve this very human problem and make sure that you're hiring the best candidate-and not the one who plays into your own biases the best? One solution is, quite literally, to take the humanity out of the equation. Artificial intelligence, or AI, has become a significant force in the HR world in the past few years. AI recruiting is a powerful tool that can feel a bit threatening (''am I being replaced by a robot?'') but in reality, it gives HR professionals a major asset in streamlining the hiring process to make it more fair and merit-based.
Use AI to eliminate diversity danger zones
Because AI screening and recruitment programs are based on data, you have the power to control what data the program is crunching. If you're concerned about hiring bias (unconscious or otherwise) entering the process, you can set the program to exclude data and diversity metrics that indicate things like age, race, sex, or location.
Studies have shown that candidates with strongly minority-identified names still receive between 30 and 50 percent fewer callbacks and interview offers than ''white''-sounding counterparts-numbers that have remained largely unchanged over the past 20 years. That there's still so much racial bias (unconscious or otherwise) in hiring is incredibly alarming-but it's fixable. An automated screening program that doesn't have our ingrained cultural and personal biases is much better at sifting through data alone.
AI metrics help ensure compliance
Alternatively, instead of excluding criteria that is rife with recruitment bias, the data can also be used to make sure that your hiring process meets industry diversity guidelines such as the Equal Employment Opportunity Commission (EEOC) guide. Some software programs are able to process applicant data, check them against best practice percentages, and ensure compliance with the EEOC regulations. All of this can be done without the human reviewer/interviewer knowing whether a candidate falls into a particular bias zone.
AI can make us better interviewers
It's not just the initial candidate screening and resume-reading phases where AI programs can help improve the process. Many companies are using AI to make their interview processes more fair and effective. Companies like Ansaro have built products that can create structured interview questions for a specific job. With a list of job-tailored questions, driven by hiring quality data rather than the individual candidate, interviewers can add an extra buffer zone of objectivity. It doesn't have to be a cold list of default questions, however-users can typically use this kind of software to work collaboratively to develop a list of questions based on best practices.
The interview is still an essential in-person dialogue to understand how a candidate interacts and thinks on his or her feet. Inviting AI into the process just means doing a different kind of prep work to help standardize the process and eliminate as much personal bias as possible. After all, you may be great at making sure your interviews are as bias-free as humanly possible-but is every potential interviewer on that same level? Consistent, impartially created interview questions can help ensure that the bias you've worked hard to eradicate stays down throughout all parts of the process.
As we move toward a more data-driven system for recruitment and hiring, it may feel like you're less able to rely on a ''gut check'' and instincts in the hiring process. But while AI may replace some of the agency that you currently have in the hiring process, it helps make important strides toward saving us from our unconscious hiring bias and diversifying your talent pool.