In an interconnected world, diversity and inclusion are necessary values for the company’s growth. A survey shows that companies with an inclusive team have made better decisions 87% of the time, and 60% have delivered better results.
Despite the clear evidence, companies show inherent bias against people based on age, nationality, race, gender, and disabilities while hiring candidates. Take Yale University’s study, for instance. The study revealed that although male and female scientists were trained to be objective, they were more likely to hire men than women and pay them $4,000 more per year. Another survey showed that 38% of recruiters admitted to reviewing resumes with bias against the applicant’s age.
Even if the recruiters are mindful, unconscious biases could unknowingly seep into the hiring process. Some of these biases are:
- Halo effect – where positive impression about a person impacts the hiring decision
- Horn effect – where a negative impression about the person impacts the hiring decision
- Confirmation bias – looking for specific information in the interview process to confirm the pre-existing belief
These biases could impact the growth of the company. A diverse workforce will bring more ideas and insights to the table and give the company a competitive edge.
While recruiters can be trained to remain unbiased, a better solution would be to leverage AI to remove the biases. AI can screen and identify candidates without prejudice and improve hiring.
How To Leverage AI To Remove Hiring Biases?
Remove bias from job descriptions
Imagine a woman receiving a job alert that’s relevant to her. She is about to apply, but the job description uses the pronoun ‘he.’ The woman would assume that the job is for male candidates and skip it. Sometimes unconscious bias reflects in the job description and job listings limiting the applications from potential candidates. These biases could be in the form of pronouns, names, and addresses. AI technology can address this problem and make job descriptions more neutral. The job descriptions are run through an AI platform that has learned to analyze and identify potential red flags through a wide range of structured and unstructured data sources. The platform will identify the description that could be biased and suggest a more neutral alternative. The balanced job description will encourage more candidates to apply for the job and enable the company to choose the right candidate.
Data-based screening and sourcing
On average, a job posting on LinkedIn receives over 250 applications. Screening each of them manually can be time-consuming. There’s also the risk of bias seeping in if the recruiter screens the CVs based on unfair parameters such as the applicant’s educational institution. Companies can use AI technologies to filter and match suitable candidates to neutralize the screening and sourcing process. The AI tool can source all the data about the candidate from various websites, social media platforms, resume databases, online job boards and notify the recruiters if the candidate matches the job description. It can also source passive candidates who haven’t applied for the role but are fit for the role. This will help the recruiters accelerate the hiring process and select the right candidate without bias.
Streamline the applicant shortlisting process
When bias enters the shortlisting process, the candidate pipeline shrinks automatically. There is a risk of good candidates getting ignored due to this flaw in shortlisting. Companies have reportedly admitted that they have reviewed only a small portion of the millions of applications they receive. By narrowing the pipeline, the companies will not be able to find the right candidate or harness their skills for growth and innovation. However, this problem can be solved with AI. Companies can use AI-enabled pattern recognition and Natural Language Programming (NLP) to review the entire pipeline and match the candidates based on their skills and experience. Some companies also leverage AI to score candidates based on their skills to remove prejudice from the shortlisting process.
Remove unconscious biases through blind hiring
Blind hiring is an anonymized technique where the candidate’s personal identity is removed from the assessment process. Any information that could lead to bias, such as age, gender, name, and ethnic background is removed from the CV. Information scrubbing can be time-consuming and error-prone if done manually. That’s where AI helps. AI can automate the scrubbing process to eliminate any chance of human error. It can also mask other information such as the alma mater, voice, and candidates’ video using sophisticated AI and ML tools. This removes all the potential scope for bias as the scoring is done using standardized analytics. The recruiters can focus on the candidates’ responses, skills, and knowledge.
Identify hidden biases
Sometimes bias percolates into the hiring process without anyone’s knowledge. To eliminate it, companies need to first find its origin in the process. AI can go through large data sets to identify the patterns of bias and identify the exact point where the bias originates. It can flag it so that recruiters can fix the issue and improve their hiring process. Eliminating bias is not a one-time process. It can seep into the process at any time. AI technologies are trained to learn more from data sets and enable the recruiters to alter the processes to ensure an unbiased recruitment process.
As the team sizes increase, recruiters will have a tough time ensuring fairness in the recruitment process. They could miss out on quality hires in the process.
X0PA has developed an AI-based SaaS platform called AI Recruiter to address this problem. The X0PA’s patented algorithm helps companies source, score, and rank the talent to find the right candidate. It also provides features such as masking personal information and videos and skill-based shortlisting to accelerate the hiring process and eliminate bias.
To know more about the AI Recruiter, contact us.