Artificial Intelligence (AI) technology is lauded as the future of complex decision making, which may explain why organizational cultures that pride themselves on being future-focused are fully embracing AI. The fact is that the productivity of the American worker has risen by 434% since 1950. To manage this increasing workload, AI-driven programs can be utilized to automate aspects of the recruiting process. Over 85% of recruiters find AI to be a useful tool in reducing workloads, improving processes, and bettering the candidate experience. Implementing artificial intelligence is not without risk, however. AI utilizes machine learning technologies to study our human behavior to shape its own insights, and where there is human behavior, there is bias that needs to be addressed.

AI Bias is Learned

AI does what it is designed to do, meaning it is only as useful to an organization’s goals as the data programming it learns from. If your AI program is fed biased information, it will regurgitate biased results. To avoid such issues, it is important that your organization thoroughly understand the data that is being used to guide your AI system.

In the initial stages of the program’s implementation, monitor its results to see if your team would have selected the same candidates as the software recommends. If you notice any biases, such as no people of color having their resume promoted, address that with your programmer. Amazon found itself facing this very issue when it discovered one of its AI programs had a bias against resumes that included terms such as “women’s studies” or “women’s university.” That led to the software being shut down. It is important to check and double check to avoid such an issue.

Programmers use sets of data to teach AI systems what to pick up on when searching for an ideal candidate. Review these training data sets to make sure biased information is not included in them. After all, AI learns from human behaviors, and humans are often unaware of their own biases.

If you are utilizing AI to generate job descriptions, make sure that gender-charged words are not included in the samples the system is learning from. There are a number of adjectives that traditionally apply to one gender over another. Start by making sure the listed job titles do not leave room for bias. Instead of seeking a “waitress” or “waiter,” look for a “restaurant server.”

If these adjustments are used in the training templates used to adapt your AI system to your business, your AI program will be better equipped to avoid learned biases. Words used to describe a position may limit who applies or what candidate your AI system identifies as hirable for the position. For example, gendered wording is a fairly well-studied phenomenon; if your team is seeking a “rockstar team player” or an “Excel ninja,” you may find that more male candidates are drawn to the role than female ones. In the same vein, using words, such as “supportive assistant” or “cooperative team member” may encourage more female applicants over male.

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AI Still Needs a Human Touch

Identifying biases in your AI’s algorithms during its developmental stage is essential to avoid a negative impact on your recruitment strategy. AI is meant to mimic a human’s touch, but not replace it.

There are some areas that require human intervention, such as resume gaps, which tend to be an obstacle for the system. AI could interpret a gap in a person’s resume as a long, questionable period of unemployment when it actually represents time taken off for child-rearing or the pursuit of additional education. In that case, programmers must prevent the system from making an incorrect judgment. Similarly, if your AI system recommends the same kind of candidate over and over, revisit your desired candidate profile occasionally to ensure a diverse group of hires over time.

The good news is that AI is equipped to learn to fit our needs. But its performance and results must be observed in order for us to identify areas for improvement. Nearly half of recruiters believe AI has helped make their workload more manageable while providing valuable insights to use in their hiring recommendations. As recruiters identify and address biases their AI systems may inadvertently pick up on, they will be better prepared to course-correct and ensure the program’s successful use for their companies.

In a competitive labor market, effective recruitment and staffing requires a data-driven approach, as most job sites optimize for the easy metrics (clicks or cost-per-click), instead of the important ones (number of hires or cost-per-hire). But we know your goal: more hires, at a lower cost.

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