The world has transformed remarkably ever since AI was introduced. Tasks ranging from coding to design are now completed with greater efficiency, unlike before. This revolutionary impact of AI is also extended to the recruitment sector. Organizations worldwide are readily embracing AI-driven recruitment strategies for simple yet crucial reasons – to save cost, reduce time, and ensure accuracy and efficiency.
According to the 2024 report by Leoforce, it is estimated that nearly 81% of companies are set to adopt AI-powered solutions to make recruiting processes more efficient. The same report also states that through automation, organizations using AI-driven recruitment tools could reduce the time required for manual tasks to 38%. While AI recruiting tools have certainly improved recruitment processes, they are also prone to biases.
Human-based recruitment is more likely biased but AI hiring tools can also be discriminatory. Since these systems rely on human-generated data, they can introduce bias in AI-driven recruitment processes. Let us explore the reasons and how to prevent AI bias in hiring.
AI Recruitment Bias – The Real Meaning
Similar to humans, AI recruitment tools can often be prejudiced when sourcing, screening, or hiring candidates. As a result, an organization’s hiring process can suffer discriminatory and unfair outcomes.
The algorithms programmed into these tools are the reason why AI recruitment tools can choose talent based on race, ethnicity, gender, country, and other factors.
How is this possible?
Well, it’s straightforward. AI tools solely function on data, the main reason why you can acquire data-driven insights about a candidate. If these data have a certain hint of bias, conscious or unconscious, the AI technology can include these preferences in its algorithm, creating biased hiring.
What are the Different Types of AI Bias in Hiring?
Surprisingly, AI bias in hiring is often practiced in various ways and it is crucial to understand each of them so that AI-driven recruitment processes are fair to every candidate. Below mentioned are the different types of AI bias in hiring:
- Selection Bias: It happens if AI tools display a preference for some candidates compared to others due to unrelated factors like educational background, demography, or even work experience.
- Stereotypic Bias: AI-driven recruitment tools can sometimes generalize candidates based on a particular behavior pattern or trait. For example, women aren’t suitable for tech or engineering roles, or older candidates aren’t technically knowledgeable.
- Performance Bias: AI recruitment tools can show performance bias when candidates are assessed based on factors irrelevant to the corresponding job performance.
Major Impacts of AI Recruitment Bias
Following a biased hiring process can signal trouble for any organization. Instead of doing good, it can call for potential consequences that could have been easily avoided. Here are the significant impacts that a business encounters as a result of biased hiring:
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Legal and Ethical Issues
Any reputable organization will want to avoid legal or ethical issues in order to retain its positive impression. However, a biased AI hiring process increases the difficulty. Several countries have anti-discriminatory hiring policies that prohibit companies from discriminating against candidates based on gender, age, race, and other factors when selecting candidates. Such policies aim to protect candidates from getting rejected merely on the basis of their identity information. Even if an organization successfully manages to avoid legal conflicts, ethical concerns may persist, which, if not addressed, could escalate into serious consequences.
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Negative Impression of the Organization
The reputation of any company, established or start-up, is as fragile as glass; easily influenced and quick to shatter. Suppose the word spreads that a company is known for following a biased AI-powered recruitment process. In that case, it can tarnish the organization’s reputation, inviting unavoidable consequences like loss of profits or inability to hire well-qualified candidates. In addition, it can disrupt the organization’s internal workflow. Many employees prioritize cultural alignment and organizational principles; upon learning that the company engages in such unfair practices, it can lead to disengagement and a decline in morale across the workforce.
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Decrease in Diversity
Organizations prioritizing diversity and inclusivity see a 35% higher success rate compared to their competitors. By embracing DEI initiatives, businesses are not only successful in terms of financial returns but can also attract top talent faster, improve productivity, and retain the best talent successfully. However, if the AI recruitment tool’s algorithm harbors bias, such as against people of color, the company can no longer be considered diverse. Besides, existing employees may feel discriminated against and choose to quit. As a consequence, declined diversity can undermine the organization’s DEI (Diversity, Equity, and Inclusion) principles while stifling innovation, creativity, and overall productivity.
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Loss of Quality Talent
Hiring top-quality talent becomes particularly challenging when organizations follow AI bias in hiring. They miss out on a vast pool of competent candidates due to the AI tool’s discriminatory algorithms. Without human oversight, it is difficult to recognize high-performing candidates, allowing valuable talent to slip away to competitors. In return, companies may end up hiring unsuitable candidates, leading to increased costs, unproductivity, and inefficiency.
Tips to Mitigate AI Bias in Hiring
While AI recruitment tools are often criticized for creating hiring bias, they are also lauded for their potential to mitigate this problem from the root. Organizations can leverage these advanced solutions by adopting the right strategies and creating a fair, equitable recruitment process. Here are the best practices to ensure unbiased hiring using AI technology.
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Human Intervention
The decision to hire a candidate should never rest solely on an AI tool. A professional team must be appointed to oversee the AI tool’s performance, especially during talent sourcing and screening. It is necessary to understand and evaluate why specific candidates are selected or rejected, ensuring the process remains accurate and bias-free. Human oversight is critical to making fair and informed hiring decisions.
Recently, a US-based IT company made headlines after laying off its entire HR team. The company struggled to find suitable candidates for over three months, prompting the manager to determine the reason. Soon, a major flaw in the ATS system was uncovered when the manager’s own resume was rejected. The ATS tool was apparently auto rejecting qualified candidates without a proper evaluation. This led to the dismissal of the HR team for failing to identify the critical issue that severely hindered the recruitment process. (Source)
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Regular Audits are Necessary
To ensure candidates are assessed without bias, it’s essential to audit AI-driven recruitment technology regularly. This helps identify and address any patterns of bias while also serving as a safeguard should there be any claims of unfair hiring practices. Besides, audits should be conducted whenever AI tools are updated so that it’s easier to catch and correct any new biases before they impact the hiring process.
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Focus on Enhancing Training Data
AI tools can’t develop biases on their own. As discussed earlier, they operate based on human-integrated data. Hence, consistently improving and diversifying the data you feed into these systems can eliminate the chances of biased hiring decisions. Let’s say an AI recruitment tool is primarily trained on resumes of only white male candidates; it may unintentionally discriminate against women or individuals from other ethnic backgrounds. However, this could be easily avoided by training the AI tool to be more inclusive, ensuring it doesn’t prefer candidates based on race, ethnicity, location, or gender.
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Notify About Biases
If the recruiting team identifies any bias patterns in the AI staffing software, it is essential to flag and report them immediately. To do this efficiently, the team must be adequately trained and educated to catch AI hiring biases before it’s too late. The goal isn’t just to stop a single instance of bias but to prevent similar cases from reoccurring moving forward. By flagging and reporting, the team can maintain a fair and equitable hiring process in the future.
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Select Diversity-Promoting Algorithms
The ultimate way to promote fair hiring practices is to select equity-focused AI algorithms. Wondering what happens when you do that? Identifying talent based on the criteria relevant to the job role becomes easier than personal attributes, religion, gender, and other unrelated conditions.
Conclusion
Technology is undoubtedly a blessing, but it can be a bane for organizations if not utilized correctly. Although AI recruitment software is designed to mimic human decision-making to assist recruiters, it’s important to remember that they aren’t real humans. In addition, AI tools can often display the same discrimination as humans. Thankfully, eliminating AI biases in hiring is possible by applying the right strategies as outlined above. To further help you in creating an unbiased recruitment process, consider contacting Tek Ninjas. Since 2015, we have provided best-in-class IT staffing solutions to numerous organizations. With access to a vast pool of highly qualified candidates, we can connect you with the perfect fit for any role across engineering, light industry, life sciences, and other sectors.