8 Ways to Overcome AI Hiring Bias and Build Stronger Teams in 2025
AI has become a core component of modern recruitment; about 87% of employers use AI to automate repetitive tasks, streamline screening, and speed up hiring. But with these advantages comes a major drawback: AI hiring bias.
Despite its promise to create a more objective hiring process, AI has shown patterns of biased or discriminatory outcomes.
For example, a 2021 research study found that Black professionals receive 30% to 50% fewer job callbacks when their resumes contain information linked to their racial or ethnic identity.
Another recent study from the University of Washington revealed that some popular AI resume screening tools favored white and male candidates, with white-associated names preferred 85% of the time.
Bias in hiring isn’t a new issue, and job seekers feel it. According to a Pew Research survey, 79% of respondents believe bias based on race or ethnicity is a major problem. However, 53% believe AI use can reduce this issue, while 13% think it will worsen it.
The reality is that AI is changing the way organizations search, vet, hire, and onboard talent, but it’s not without challenges like bias. In this guide, we’ll share ways to address this drawback and use AI tools correctly.
What Is AI Hiring Bias?
AI hiring bias refers to the systematic and unfair discrimination in AI systems used in the recruitment process. Because of this bias, AI models screening resumes and selecting job applicants may favor certain groups. The outcome of this bias can negatively impact diversity and inclusion efforts.
Although the goal of AI-based recruitment tools is to streamline candidate selection and reduce human biases, AI models can unintentionally replicate or even amplify existing biases in the data they’re trained on.
Famous AI Hiring Bias Example
A notable example of AI hiring bias is Amazon's infamous AI recruiting tool, which was found to discriminate against female candidates. The system was trained on resumes submitted over a ten-year period, predominantly from male applicants, as males dominated fields like software development.
Amazon’s AI recruiting model learned from the data and downgraded resumes that included the word "women's" or were from all-women's colleges. This led to qualified female candidates being unfairly overlooked in the hiring process.
In this case, the bias wasn’t intentionally programmed into the model. Rather, it emerged from biased training data, a reflection of the male-dominated applicant pool. The AI simply learned to mimic the patterns in the data, ultimately reinforcing gender bias instead of eliminating it.
What Contributes to AI Hiring Bias?
AI isn’t inherently biased toward a specific gender or group of people. However, it can become biased based on the data it’s fed and how it’s designed to make decisions. Also, human biases can also make their way into AI–after all, it’s humans powering these models.
1. Biased Training Data
AI systems learn patterns and make decisions based on the data they're trained on. If this training data contains existing biases, the AI will likely replicate them, resulting in biased outcomes.
For instance, if historical hiring data favors male candidates, an AI-driven recruitment tool may inadvertently prioritize male applicants over female candidates, as was the case with Amazon’s hiring tool.
Bias in training data can extend beyond hiring, too.
Sony researchers found that common facial image datasets overrepresented individuals with lighter, redder skin tones, and underrepresented those with darker or yellower skin. This imbalance led to several AI systems, including Twitter’s image cropper and other image-generating algorithms, favoring redder skin tones.
Skewed training data can lead AI to form distorted patterns, even in hiring. That’s why diverse, representative data is essential for fair outcomes.
2. Algorithmic Design Flaws
The architecture of an AI algorithm significantly influences its decision-making process. Algorithms designed without fairness constraints may amplify patterns of bias present in the training data (again, as we saw in Amazon’s case).
For example, if an AI-based hiring tool is programmed to identify candidates similar to previous successful employees, it may knowingly favor candidates from the same demographics. And this could eventually result in biased decisions.
3. Human Involvement in AI Systems
Human biases can infiltrate AI systems during their development and deployment. Developers' unconscious biases may influence the selection of training data, the setting of algorithmic parameters, or the interpretation of AI outputs.
Mathew Renick of Korn Ferry says, “It’s not that the AI tools themselves perpetuate bias, but rather the human input and utilization of them.”
On the contrary, a lack of human oversight can also be a problem. Recruiters who blindly rely on AI recommendations without critical evaluation may miss signs of bias, which allows flawed decisions to go unchecked.
Most Common Consequences of Biased AI in Recruitment
AI-driven recruitment tools are designed to improve efficiency and reduce time-to-fill, and in many cases, they succeed. They’re also intended to be more objective than human recruiters. In fact, a survey found that 68% of recruiters believe AI can help eliminate unintentional bias.
However, that ideal isn’t always the reality. When bias exists in AI systems, the consequences can be serious, both for organizations and for job seekers such as:
1. Missing Out on Qualified Talent
Biased AI algorithms can unintentionally filter out suitable candidates, which can lead to a smaller talent pool.
A Harvard Business School study highlighted that AI algorithms might lead to missed opportunities for “hidden” talent—candidates who may be well-qualified but have been out of the workforce. These include individuals such as stay-at-home parents, veterans, or those with non-linear career paths.
2. Reduced Return on Investment (ROI)
The efficiency promised by AI-powered solutions in recruitment can be undermined by biased outcomes, negatively impacting ROI. When AI-based tools produce unfair outcomes or inaccurate hiring results, companies often need to invest additional time and resources to identify and fix these issues. This ultimately defeats the purpose of automation.
Moreover, recruiting less suitable candidates due to biased decision-making can result in higher turnover rates, additional expenses related to rehiring and training, and legal troubles like prosecution or fines.
“According to the EEOC’s lawsuit, iTutorGroup programmed their tutor application software to automatically reject female applicants aged 55 or older and male applicants aged 60 or older. iTutorGroup rejected more than 200 qualified applicants based in the United States because of their age.” U.S. Equal Employment Opportunity Commission (EEOC)
How to Overcome AI Hiring Bias?
Addressing bias in AI-driven recruitment processes is necessary for equitable hiring outcomes and realizing the real benefits of these technologies in hiring. Here are eight ways to comprehensively address bias issues in AI-driven talent acquisition:
1. Diversify Training Data
First and foremost, it’s crucial to detect and remove any biases from the training datasets on which AI-based recruitment tools are trained. These systems should be trained on diverse and representative datasets to make informed, objective decisions.
For instance, incorporating data from candidates of various genders, ethnicities, and backgrounds can help the AI make more balanced decisions.
Diversity in datasets isn’t just good for removing bias, but can also positively impact the perception of technology users. A study by Penn State University found that displaying racial diversity cues in AI interfaces can boost users' perceptions of fairness and trust in the system.
According to S. Shyam Sundar, Evan Pugh University Professor and director of the Center for Socially Responsible Artificial Intelligence at Penn State, “AI training data is often systematically biased in terms of race, gender and other characteristics. Users may not realize that they could be perpetuating biased human decision-making by using certain AI systems,”
2. Implement Algorithmic Fairness Techniques
Incorporating fairness constraints into AI algorithms can help prevent biased outcomes. Techniques such as re-weighting training data or adjusting decision thresholds can promote equitable treatment of all candidates.
Researchers are actively exploring methods to ensure that prediction models are as accurate and unbiased as possible. A paper from the University of Washington looked at the current literature on addressing AI hiring bias.
According to the paper's author, researchers suggest involving diverse stakeholders in the design process of such algorithms, conducting more qualitative research, and monitoring and evaluating the systems' performance to ensure that their choices are free of bias.
3. Conduct Regular Bias Audits
You may not get everything right on the first go, even with the diversity of data sets and neutral algorithms. That’s why it’s important to audit AI systems for bias regularly.
Tools such as Google’s What-If and IBM AI Fairness 360 can help you audit your AI recruitment tools and uncover any lingering biases.
In some places, such audits are a requirement. For instance, New York City has mandated recruiters who use automated employment decision tools, or AEDTs, to conduct a bias audit at least within a year of their use. Such legislation is a good thing, as it requires companies to revisit and analyze AI tools for hiring and ensure their decisions are fair and inclusive.
Sam Shaddox, Vice President of Legal for SeekOut, says, "Performing a bias audit also is an integral part of not just our broader responsible AI program but also our approach to compliance."
4. Appoint AI Ethics Officers
Designating AI ethics officers can provide oversight and ensure AI systems adhere to ethical standards. Such officers may monitor AI deployment, address potential biases, and ensure compliance with legal and ethical guidelines. They can add that ‘human’ element to the hiring process to ensure those nuances in hiring decisions are still addressed.
Organizations are increasingly recognizing the importance of such roles in maintaining fairness in AI applications. According to Business Insider, AI Chief Ethics may just be the most important AI job. That’s because companies aren’t just using AI for recruitment, but for various other business processes, where ethical issues like bias can occur.
A dedicated ethics officer, especially in an executive capacity, ensures oversight and necessary ethical and compliance. They can lead audits, keep tabs on regulations, and engage stakeholders to make sure the use of AI technologies doesn’t result in unfair and unethical practices.
Companies like Microsoft, IBM, Salesforce, and Accenture already have designated AI ethics leaders.
5. Provide Counter-Stereotype Training
AI algorithms for candidate screening and interview analysis should be trained to mitigate unconscious biases. Counter-stereotype training for both AI systems and human recruiters can help with that.
This typically involves exposing AI models to diverse examples that challenge existing stereotypes. This way, organizations can promote more equitable candidate evaluations.
For example, in Amazon’s case, its recruitment tool was trained on data that heavily favored male applicants because that was simply the case (there were more male software engineers during that period).
However, the algorithm unknowingly became biased and perpetuated the stereotype of software developers mostly being men. With counter-stereotyped training, such a conclusion may have been avoided.
Interactions with humans in training and using AI technologies can play an important role in this matter. Researchers from Harvard, Wharton, and ESCP Business School in Berlin suggest, “Humanizing and socializing AI can reduce prejudice through more repeated, direct, unavoidable, private, non-judgmental, collaborative, and need-satisfying contact.”
6. Increase Transparency in AI Decision-Making
Many AI-driven recruitment tools operate as 'black boxes,' with opaque internal decision-making processes. This lack of transparency raises concerns about fairness and accountability in hiring decisions.
Being open about exactly how your recruitment tools work (without revealing secrets or data) can help increase trust in the process and highlight any biases before they become more widespread.
According to Forbes, employers must openly communicate their use of AI in candidate assessments, and vendors should disclose the AI tools employed.
7. Create Diverse Development Teams
Harvard Business Review emphasizes that to combat bias in AI, companies need more diverse AI talent. It also raised the point that the tech industry has a diversity problem, with fewer people of color in the workforce.
Building AI systems with diverse development teams can reduce the risk of embedding unconscious biases into algorithms. For example, algorithms designed by men exclusively may unknowingly give an advantage to other men.
Even if your team isn’t that diverse, ensure that feedback from various groups is considered during the development or deployment. That can help identify any biases that may have gone unnoticed or detect the potential for them.
8. Establish Ethical Guidelines for AI Use
Last but not least, create a comprehensive policy around the use of AI in hiring. Use the latest findings in research on dealing with AI biases and consider the rules and regulations of using such software solutions.
These guidelines should address issues like data privacy, consent, and mitigating biases to promote fair hiring practices.
Adhering to ethical guidelines for AI deployment in recruitment ensures that AI-based employment tools are used responsibly. Make transparency, fairness, and accountability the cornerstones of your AI use policy.
Here are the key elements of a sound policy on ethical use of AI in hiring:
Data diversity
Explainable AI
Human oversight
Bias mitigation
Regular audits
Data privacy
Clear communication and responsibility
Training and ethical guidelines for users
Should There Be Human Oversight in AI Hiring?
Yes, there should be some human oversight in AI hirings. For example, a human recruiter may review an AI algorithm's decision. The tool's explanation of its decision and how or why it got there may help a human in charge detect any possibility of bias and investigate further.
Integrating AI-based tools in recruiting offers many benefits like efficiency, cost savings, and competence.
According to a survey by HireVue, 67% of HR professionals say AI is just as good or even better than humans at finding well-qualified candidates. That said, it can’t and shouldn’t entirely eliminate human input from the process.
More importantly, candidates may also prefer human involvement in the hiring process. 68% of respondents in a Tidio survey said they want a human presence in the final hiring decision.
Then there are the legal implications that further increase the need for human intervention. No oversight from an experienced recruiter increases the risk of biases, which, in turn, raises the risk of non-compliance, fines, and reputational damage.
This balance or human-AI synergy can be strengthened by partnering with recruiters or agencies who understand your specific industry, whether it’s tech, healthcare, hospitality, or engineering. They bring the context, judgment, and empathy that AI alone can’t replicate.
Integrate AI and Human Collaboration in Hiring
The key to making the most of AI in sourcing talent while mitigating biases is to maintain that human element throughout the technology's journey, from its training to its actual use in organizations.
Not using AI tools for recruitment is counterintuitive. However, it’s essential to address any drawbacks, including bias, with the correct approach. That will help you avoid discrimination and tap into the benefits of using AI in hiring.
Here’s how Dominique Virchaux, President, Global Consumer Practice, South America, Korn Ferry, puts it:
“Define the steps of your recruitment process clearly. Automate anything that doesn’t impact the candidate experience or require judgment. Then, refocus the human effort on key moments that matter—for example, interviews or making offers.”
FAQs
What is an example of AI discrimination in hiring?
An example of AI discrimination in hiring is Amazon’s experimental AI recruiting tool, which showed bias against female candidates. The system was trained on resumes submitted over a decade, mostly from men, and learned to penalize resumes that included terms like “women’s” or referenced all-women’s colleges. As a result, qualified female applicants were unfairly downgraded.
How does AI affect hiring?
AI affects hiring by automating various recruitment process stages like resume screening, candidate sourcing, and interview scheduling, which helps reduce time-to-fill and improve efficiency. It can analyze a large talent pool to identify suitable candidates based on skills and experience. However, if not properly monitored, AI can also introduce or amplify bias in the hiring process.
Is bias in AI legal?
Bias in AI hiring can lead to legal ramifications for employers. The Equal Employment Opportunity Commission (EEOC) enforces federal laws prohibiting employment discrimination in the U.S. Additionally, various states have introduced legislation to regulate the use of AI in hiring, requiring transparency and bias audits to prevent unfair outcomes.
Employers using AI-based hiring tools are responsible for ensuring these systems do not result in discriminatory practices.
What is an example of a biased feedback loop in AI hiring models?
A biased feedback loop in AI hiring occurs when a model is trained on past hiring data that reflects human biases, such as favoring a certain gender or background. The AI learns to prefer those traits and recommends similar candidates. When these candidates are hired, their data reinforces the original bias, creating a self-perpetuating cycle of discrimination.