Introduction
Hiring bias! That one frustrating barrier that keeps top talent hidden from recruiters. Whether it’s conscious or unconscious, bias can lead to missing out on exceptional candidates who might have been a perfect fit for the position you’re hiring for. Not just that–imagine how your candidates perceive your brand when they have biased experiences with you in a world heading more toward Diversity, Equity, and Inclusion (DEI) every day! That one wrong move could cost you top talent, money, time, and whatnot. If words are not enough, here are some statistics on hiring bias to give an idea of how it affects recruitment, ROI, and candidate experience.
- 90% of candidates believe recruiters show bias when hiring. (SME)
- 37% of Americans say bias on job applicants in hiring is a major problem. (Pew)
- 48% of HR managers admit bias influences their candidate choice. (LinkedIn)
- Hiring bias costs U.S. employers $64 billion annually. (Gitnux)
Favoring candidates based on looks, race, or age? Hiring them because they studied at the same university as you or they’re your friend’s friend? Not ethical. Especially with today’s focus on diversity and inclusion, eliminating bias is essential to attracting qualified candidates and building a diverse talent pool.
In this blog, we’ll discuss more about the types of bias that often occur in the hiring process and how automation and AI technologies could help you wipe them off completely. Also, some impacts to show you that people worldwide have already leveraged these technologies to reduce bias in recruitment. Are you ready to do the same? Read on.
What is Hiring Bias?
Hiring bias or recruitment bias occurs when your hiring decisions are influenced by systematic errors or prejudices. More often, Bias in hiring causes recruiters to lose their best candidates to their competitors while tarnishing the employer brand and values along with it. Since recruiting involves making quick decisions, bias can tread in most of the time and put blemishes on your otherwise perfect recruitment process. Let’s see what type of recruiting bias can get in the way of your hiring.
Types of Biases in Recruitment
1.Cognitive Bias
Thoughts are integral to making decisions. But when it comes down to an individual, subjective thought patterns and mental shortcuts can cloud judgment. For instance, when you’re tempted to look for information to reject a job seeker (confirmation bias), when the candidate’s first impression sucked and you make an evaluation based on just that (first impression bias), or when you compare candidates instead of assessing them individually (Contrast bias). All these biases take you far away from the right candidate and a standard recruitment process.
2.Appearance Bias
Well, everyone knows this one. Hiring by judging candidates based on their physical traits rather than their qualifications or skills is the recipe for a bad hire. This happens when you don’t like the candidates because they’re too skinny or too tall (Height/weight bias), if the candidate’s big nose or nervousness is a problem for you even though he/she fits the role well (Horn effect), or you pick the wrong candidates only because they dressed well! The consequences of such biases become apparent within the first week of onboarding when you realize they may look good but won’t get the job done.
Explore: 90 Day Onboarding Plan for new hires
3.Affinity Bias
We all tend to like people we can relate to in some way or another. However, when it comes to hiring, this can lead to unintended bias. Imagine interviewing two candidates—Sasha, who is an average fit for the role, and Tina, who is a great fit. Yet, you end up choosing Sasha simply because you both went to the same elementary school. Even saying it out loud sounds ridiculous. Hiring is an expensive process that impacts the entire organization, so it’s crucial to ensure you don’t select candidates based on perceived similarities such as shared hobbies, backgrounds, or alma maters.
4.Demographic bias
This is an easy one! How many times have you hired candidates based on their name, age, gender, ethnicity, race, etc. when in reality none of these factors have anything to do with the person’s ability to perform the role well? If it’s a no, good for you. If it’s not, things need to be changed. Treating your candidates unfairly based on such characteristics does not support inclusive hiring, a trend that is picking up pace among Gen Z candidates (Gen Z talent is most in demand now, and you don’t want to make a bad impression on them, do you?).
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5.Perception Bias
Judging candidates through the angle of subjective perceptions, peer pressure, or surface-level cues comes under perception bias. To break it down–you reject a candidate because you think their overqualification will make them leave the role soon (Over qualification bias), you’re one of the three interview panelists but your objective assessment contradicts their subjective evaluation forcing you to agree to their decision (Conformity bias) or assuming a candidate is not confident because he/she was fidgeting during the interview (nonverbal bias).
How Automation & AI reduce Hiring Bias
1.Blind Screening
As a recruiter who wants to keep the hiring process as bias-free as possible, blind screening assisted by AI/Automation can be a boon. AI will remove all the bias-causing details like name, race, and other identifying details from resumes so that you can solely focus on the candidates’ qualifications, skills, and experience. An objective way of making decisions indeed, isn’t it? This ensures unconscious bias is kept at bay, allowing you to choose talent based purely on merit.
As per a Cornell University article, many major companies in the UK including HSBC, Deloitte, Virgin Money, and KPMG have implemented blind screening by hiding the names and even the names of the schools candidates have attended. They all have seen a pattern of reduced hiring bias through this.
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2.Standardized Assessments
Structured interviews and skill-based assessments are some of the best practices to maintain a consistent and objective evaluation process in hiring. By following this, you can make sure that every applicant who enters your pipeline is assessed based on the same benchmarks and leaves no room for any sort of biased decisions. The objective angle over the subjective angle in evaluations will do the part of removing bias intrution to a great extent.
Explore: A Complete Guide to Skill-evaluation tests
3.Diverse Training Data
One of the best things about involving AI models in recruitment is the fact that they’re trained on diverse datasets. It covers different demographics, geographies, and industries, thus knowingly or unknowingly includes ‘inclusivity’ in the process. So, such models have access to historical data as well, which gives them a fair idea of the type of perpetuating historical biases to watch out for. Hence, there will be lesser replication of such biases found in legacy recruitment practices.
Explore: Data-driven Recruitment- A Complete Guide
4.Bias Detection Algorithms
Here’s a scenario: You’re urgently composing a job description for your next hiring spree, trying your best to keep it as bias-free as possible. But as humans, we don’t always notice what’s hiding in plain sight—it could be a simple gender-coded word like “dominate,” and suddenly, your attempt to create an unbiased job description is compromised. Even with the best intentions, mistakes happen.
This is where bias detection algorithms come in handy. They can save you time and effort (and yes, your day!) by flagging discriminatory language or providing real-time insights into biased patterns. These tools help you maintain neutrality, not just in job descriptions but throughout the recruitment process, ensuring it remains inclusive and appealing to everyone.
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5.Consistent Scheduling
There are two sides to this. In manual scheduling, you may show unintentional favoritism toward certain candidates by scheduling their interview quicker and leaving the others waiting. The same can happen when recruiters assign interview panelists unevenly—overloading some members while others relax and chill. This should not happen if you want your recruitment bias-free. To avoid this, you can automate interview scheduling and follow-ups so that all candidates end up having equal opportunities for interactions with your recruiting team and panelists won’t be facing interview burden.
At Hyreo, we integrate load balancers into our Intelligent Interview Scheduler, which automatically collects availability data from panelists’ calendars. It schedules interviews in a way that evenly distributes the workload. Interesting, isn’t it?
Explore: A Guide to Automated Interview Scheduling
Key Statistics on Reducing Bias
Not going any further without giving you some numbers to how AI and automation is reducing hiring bias for organizations. Where words don’t work, numbers do!
- AI assesses the entire pipeline, not just the 10-20% typically reviewed by humans, ensuring equitable evaluation of applicants (HBR).
- 68% of recruiters believe AI reduces unconscious bias (Forbes).
- 47% say AI evaluates applicants more fairly than humans (Pew Research).
- AI detects and removes gender-biased language in job postings (SAP).
- 27% of companies deploying AI are actively reducing bias in their systems (IBM)
- 70% of organizations leverage AI for streamlined processes and reducing bias in hiring. (QX Global Group)
Conclusion
In the AI era, you’re no longer stuck with legacy recruitment practices or systems. You now have the power to break free from outdated, unproductive patterns—like hiring bias—by embracing technology. That’s exactly what innovation-driven professionals strive to do.
If you’re ready to see this transformation, try Hyreo and experience the change yourself. Not only does this AI-powered recruiter co-pilot promise a bias-free approach at all stages of recruitment (Ahem! Our n AI-powered Talent Profiling and Prescreening tool is designed to keep bias at bay), but it also gives you tools and capabilities to connect more efficiently with your top talent and get your open roles filled with ideal candidates in record time.
Get in touch with one of the experts at Hyreo to have a walk-through of everything we do in a Quick Demo.
FAQs
- How does AI help in reducing hiring bias?
AI reduces hiring bias by removing bias-causing details from resumes, using standardized assessments, detecting discriminatory language, and ensuring equitable evaluation of candidates.
- What is blind screening in recruitment?
Blind screening eliminates identifiers like name, gender, and ethnicity from resumes, allowing recruiters to focus solely on skills, experience, and qualifications.
- How does Hyreo’s AI-powered platform ensure unbiased hiring?
Hyreo leverages Generative AI for talent profiling and prescreening, integrated with bias-detection algorithms and intelligent scheduling tools to ensure an equitable, transparent hiring process.
