Eliminate Unconscious Bias in Recruiting: Importance of Predictive Data

Data analysis is most likely an essential part of your business activities. Analyzing candidates’ skills, job performance, and career goals can help you find the right hires. However, relying solely on human judgment to determine which candidates are interviewed can create unconscious bias in your recruiting system. Hiring managers may tend to favor candidates with beliefs, educational experience, background, or other characteristics similar to their own. Rather than hiring for experience and qualifications, managers may implicitly focus on other traits when making hiring decisions. Fortunately, predictive data can determine the potential success rate of your job candidates through an unbiased process. Here are a few ways how.   

Predictive Validation 

Job analysis and predictive validation help to determine which candidates are more likely to be successful in a role. For instance, Pymetrics gathers personal and professional characteristics of top performers in a given role to create a baseline. This baseline shows the traits that make someone successful in the position. The traits are mapped to the job being performed to determine why they’re important. Once the algorithm for success in the role has been active for a while, performance data, retention data, and other information are gathered to validate the findings and predict which candidates will be successful in the role. To ensure bias isn’t present, the algorithm is run on people hired as a result of the predictive validation process. Men and women, people of various ethnic backgrounds, and other differences should receive equal pass scores. If not, the data is reevaluated to determine what’s causing the bias and how it can be fixed. Once changes are made, the process is tested again until no bias shows up.  

Targeted Candidate Searches 

Predictive data lets you conduct targeted candidate searches to fill open roles. For instance, HiringSolved provides software that searches the web for publicly available candidate data, then compiles it into candidate profiles. Because information on the Internet is regularly updated, the profiles contain the most recent candidate data available. This process looks for specific relevance layers to find required talent. The algorithms rank potential candidates based on information from their public profiles and its relevance to the search parameters of the job description. Bias is eliminated because only the most important information is looked at to determine which candidates should be contacted for interviews.    

Relevant Characteristics 

Predictive data focuses on more than just skills and experience when qualifying candidates. Innovation, adaptability, communication, and other traits not found on a resume are important for success in a role. Predictive data also takes into account personality, problem-solving ability, social intelligence, and other factors to determine which candidates are best suited for a role. This process reduces bias in recruiting by focusing on a candidate’s entire self rather than a handful of specific areas.  

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