1. 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, Hiring Solved 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.
2. Choosing the Right AI Solution
Creating a diverse workplace is among your top priorities. One way to do so is by implementing artificial intelligence (AI) into your recruiting process. Using AI can help you base your hiring decisions on candidates’ skills and qualifications rather than on implicit stereotypes about their experience and background. Find out how choosing the right AI solutions to help eliminate unconscious bias in your recruiting process.
- Using Skills-Based Tests
Rather than using a resume to assess a candidate, AI-based software can use skills-based tests to choose which candidates to interview. For instance, Pymetrics builds machine-learning algorithms that have your top candidates play neuroscience games to test for short-term memory, planning, responding and other traits needed to carry out job tasks. Results are analyzed by bias-tested algorithms created from your top performers in the role. Pymetrics provides you with a recommendation for each candidate’s predicted fit for the role. GapJumpers uses blind auditions, and skills-based tests to determine which candidates get called in for interviews. This increases the number of women and people from diverse educational backgrounds, not just Ivy League schools, who are hired for jobs.
- Making Candidates Anonymous
AI can make candidates anonymous. This helps recruiters make hiring decisions based on knowledge, skills, experience, and qualifications rather than other factors. For instance, Entelo redacts names, photos, gender, schools, graduation dates, and additional information that can lead to a preference for or against a candidate. Search Party brings up anonymous candidate profiles with enough data to make an educated hiring decision free from gender, ethnicity, and other bias-inducing information. Hiring managers use the remaining information to determine how many candidates they have, which candidates were selected for interviews and why, who interviewed the candidates, and what the outcome was. No matter the results, the right candidates were interviewed, and the best was hired.
- Implementing the Implicit Association Test
Hiring managers can use AI to uncover and work to correct their biases. For instance, the Implicit Association Test uncovers thoughts that managers unconsciously hide from themselves and measures attitudes and beliefs they may be unwilling to report. The test measures the strength of associations between a concept, such as sexual orientation and evaluations, such as good or bad, and stereotypes, such as stylish or clumsy. Managers can use this information to correct their biases before interviewing candidates.
3. Job Descriptions
As with many companies, unconscious bias may exist in your job descriptions. This might encourage one group of candidates to apply more than another group. Implicitly turning away groups of qualified candidates is not good for your organization. You lose out on skilled candidates who are a perfect fit for your team. As a result, you want to make your job descriptions as free from bias as possible. Here are three ways to do so.
- Write Inclusive Job Descriptions
Job descriptions often provide candidates the first impression of your company culture. For this reason, you need to use appropriate word choices to provide the desired impact. For instance, avoid using words such as “competitive” or “determined,” which many women perceive as meaning they don’t belong in your work environment. Don’t include “collaborative” or “cooperative,” which often turn away men. Instead, replace stereotypically gendered words with more neutral tones, or balance the number of gendered descriptors and verbs. For instance, go back and forth between the words “build” and “create” to attract both female and male candidates.
- Include Fewer Requirements
Reduce the number of requirements in the job description. Although men typically apply for jobs when they meet 60 percent of the requirements, women typically apply if they meet all of the requirements. As a result, listing too many job requirements can turn away female candidates if they don’t feel qualified enough to apply. To avoid this, list only the requirements necessary to perform the work. You’ll attract a wider variety of candidates.
- Implement Software
Use software created to reduce bias in job descriptions. For instance, Textio uncovers key phrases, spots biases, and provides feedback on job descriptions as you type them. The software highlights words and phrases, then classifies them as negative, positive, repetitive, masculine or feminine. This helps you avoid words such as “rock star,” “ninja,” or “killer,” which tend to turn away women. The software also provides insight into the strengths and problems with your job descriptions, such as good use of active language or too many clichés or jargon. You receive a score for each job description along with recommendations for improvement.
- Use Gender Decoder for Job Ads
Another example of software designed to reduce bias in job descriptions is Gender Decoder for Job Ads. Although the likelihood of men applying for roles with feminine-coded job descriptions such as “agree,” “honest,” and “support” is slight, women are far less likely to apply for roles with masculine-coded language such as “active,” “independent” and “opinion.” The software guides you in using more neutral words to create job descriptions and attract a more even number of female and male candidates.