Here comes another consequence of the ongoing global pandemic: Employers can expect to see a marked increase in age discrimination lawsuits in the next six to twelve months, according to experts cited in the American Bar Association (ABA) Journal. Certain people face greater pandemic-related risks than others due to age and underlying health issues, health experts have warned. After the workforce shakeup due to economic shutdown, early signs are that older workers have faced layoffs and non-recall to the workplace at a higher rate. They may also have a harder time finding a new job in the post-pandemic environment. Age bias may have an influence on the structure of workforces in 2021 despite federal laws prohibiting age discrimination in hiring.
There are other types of bias in hiring that can the affect the makeup of your workforce as well. Three common types of bias are:
Confirmation bias – With this type of bias, interviewers seek out information that confirms a pre-existing belief. They form a positive or negative impression based on one or two details about the candidate, and then ignore or discount other details that challenge their first impression.
Halo/horns effect – This type of bias happens when an interviewer assumes one detail can be generalized across the rest of that candidate’s information. A prime example of the halo effect in hiring is assuming that because a candidate worked for a top competitor, that candidate must have strong skills. With the horns effect, the interviewer assumes all of the candidate’s characteristics are negative based on one undesirable detail. The halo/horns effect is a common first-impression bias.
Similarity bias – People are drawn to others who have similar superficial characteristics, like race, political perspective, or sense of humor. These may be a good basis for friendships, but they shouldn’t be the basis for hiring. Similarity bias is one of the most influential biases in hiring because it is a direct cause of lack of diversity in a workforce.
Each of these biases can be hard to block out of the hiring process because they are types of unconscious bias. Even when hiring teams try to stay aware and avoid them, they can sneak in.
Reduce hiring bias with predictive analytics
Organizations can reduce bias by implementing a scientifically-designed hiring process that uses data and predictive analytics to support hiring decisions. There are three essential elements in this process:
- Modern data collection approaches such as questionnaires and assessments that can be quantitatively scored and validated, rather than relying on resumes, which are a known source of bias.
- The use of predictive analytics, including advanced artificial intelligence techniques to identify complex patterns in data and relate pre- and post-hire data points.
- An active feedback loop that continually supplies post-hire data points that can be used to adjust the predictive analytics models.
Modern Hire makes it easy for recruiting teams to implement this type of process with Hiring Blueprints. Blueprints are prescribed workflows that leverage the Modern Hire enterprise hiring platform, which integrates predictive analytics, artificial intelligence, pre-hire assessments and automation. A scientifically-designed, validated hiring process puts the focus on predicting candidates’ on-the-job performance, enabling hiring teams to use data rather than instinct to drive unbiased candidate selection.
Optimize your workforce with science and technology
The next step forward for organizations that prioritize workforce diversity is collecting and tracking protected class data in a systematic, quantitative manner. In the past, some organizations have intentionally steered clear of group membership data for a variety of reasons. This head-in-the-sand approach can’t work for organizations genuinely pursuing workforce diversity.
The viable alternative is to use this data to uncover bias and then adjust the algorithms being used as the foundation for a data-driven hiring process to eliminate it. Modern Hire works with organizations on this process. We use rigorous, applied science to measure bias and deploy augmented intelligence that enables hiring team to align hiring decisions and outcomes with their organizational goals.
To find out more about understanding and evaluating the science and technology to reduce bias in hiring, download our whitepaper, Data-Driven Selection for a More Diverse Workforce. It describes the types of data you need to collect to assure a less biased hiring process, and ways certain hiring tools like resumes actually promote bias. Learn more about the role of modern data collection and analysis in creating a more diverse and high-performing workforce for your organization.
