Does AI Actually Remove Unconscious Bias from the Hiring Process?

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Across almost every single industry, artificial intelligence (AI) promises to create efficiencies, streamline workflows, automate manual tasks, and improve processes. Hiring and recruiting are no exception.

From new applicant tracking systems to algorithms that predict candidate success, AI promises to change everything you know about recruitment. For hiring managers, this claim to reduce sourcing time and to accelerate time-to-hire sounds like a dream.

However, there are very real concerns when it comes to overreliance on AI during the hiring process. Job seekers are often wary of artificial intelligence tools and concerned they could be overlooked simply due to AI.

Ultimately, machine learning algorithms, natural language processing, and computer vision programs are all AI-based tools created by humans. They are susceptible to normal human error, like unconscious bias. For the recruitment and hiring process, this can be a concerning issue.

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What Is Unconscious Bias?

Unconscious bias is an involuntary prejudice that lives outside of our conscious awareness. It occurs when our brain makes snap judgments and assumptions based on our previous experiences. Many people are unaware of their unconscious biases, making these biases difficult to eliminate. In recruiting and hiring, unconscious bias has detrimental consequences not only on well-qualified candidates, but also on the overall diversity of a workforce.

RELATED: What is a Blind Hiring Process—And is It Right for Your Business?

Concerns Around Overreliance on AI in the Hiring Process

While AI can streamline initial screenings, monitor social profiles, and pick up important keywords within resumes, it can also increase biases and discrimination within the hiring process. Here are some reasons to question if AI removes unconscious bias from hiring.

Data Bias

The majority of AI innovation relies on large data sets to learn and to make predictions. Unfortunately, this data can inadvertently lead to bias within an AI program. One way this can happen is when a particular demographic is underrepresented in the data set.

This can lead to AI discriminating based on gender or race. If an algorithm predicts candidate success, it might look at the current demographic makeup of a company and inadvertently prioritize candidates of the same demographic.

For example, a well-known Fortune 500 company scrapped a recruiting tool that showed an extreme bias against women. How did this happen? The AI learned from a data set of resumes submitted to the company, which mostly came from men. This led the AI to conclude that women weren’t good candidates.

Bias in AI can also develop if the AI learns from a biased data set, which can easily happen if only one person is collecting or analyzing the initial data set. If only one person chooses which data to include and exclude, any unconscious bias could negatively affect what the AI learns.

To avoid creating a bias from a single individual’s point of view, companies should source diverse perspectives and opinions across the entire company. For example, instead of a single individual training AI to look for specific phrases, different people from the company could offer their own wording to make sure it’s capturing a variety of unique voices.

Data bias leads to the next potential problem: algorithmic bias.

Algorithmic Bias

Algorithms learn based on input and predictive technologies, which they use to generate certain outputs. If they learn based on biased input, then they’ll produce biased results. In the hiring process, algorithmic bias can steer companies toward certain candidates by replicating historical biases.

For example, an AI algorithm could learn to prioritize resumes with a university degree, even for roles that don’t require a degree. This could eliminate candidates without expensive, formal education.

Erasure of the Human Element

While some AI-based video interview software promises to assess everything from a candidate’s word choice to their speech pattern and facial expressions, nothing beats the intuition of expert recruiters.

At the end of the day, human workers work alongside other human team members, and AI simply cannot guess at culture fit, personality integration into the team, and more. An overreliance on AI replaces the human-to-human element of the talent acquisition process, potentially creating biases and poorly qualified candidates.

Related: 10 Jobs in Artificial Intelligence (AI) You Should Consider Hiring

Balance Innovative New Technologies and Human Expertise

AI absolutely offers the opportunity to speed up the talent acquisition process, offering everything from faster candidate sourcing to automated interviews. Plus, the excitement and buzz around AI aren’t going anywhere anytime soon. A 2022 study found that 63% of companies are investing or planning to invest in AI solutions. However, these are best used when balanced with expert recruiting.

Expert staffing support offers the best of both worlds. Save valuable time by connecting with our expert recruiters. And if you’re looking to remove bias from the hiring process, our DEIB team provides both DEIB training and DEIB recruiting.

Partner with Insight Global to build a truly equitable and inclusive workforce through education, perspective, and diverse practices. Through in-depth training and expertly curated curriculum, DEIB consulting prioritizes human interactions and relationships.

Innovative companies can use a variety of new technologies at their disposal to improve their talent acquisition process, and AI is just one of many. However, overreliance on AI should be avoided as human expertise and relationship building are still at the forefront of recruitment.

Connect with Insight Global today to learn more about our training and talent solutions.