Artificial intelligence (AI) has revolutionized the world of recruiting. From optimizing job descriptions to parsing resumes, AI has been able to help free up time for recruiters to focus on what matters – attracting and retaining the best-fit candidates for your organization.
AI tools can have a lot of benefits for recruiters, including the potential to help mitigate the unconscious biases that can negatively impact recruiting. While it’s true that AI can help identify hiring bias, the reality is that AI is trained on human data, and can therefore be biased itself.
Not properly vetting and fact-checking AI tools during the recruiting process can lead to biased hiring decisions – and even open your organization up to legal issues, such as discrimination claims.
To help make fair hiring choices – and keep your organization shielded from legal issues – we’ve put together a guide to navigate and mitigate the potential AI hiring biases.
What Is AI Hiring Bias?
Just like humans, AI algorithms and software can be biased. So, when organizations rely on AI during the hiring process, the result can be hiring discrimination and other unfair outcomes due to bias.
AI algorithms can make biased decisions about candidates based on factors like race, ethnicity, and gender, among other things.
For example, a recruitment program made by Amazon in 2018 had to be shut down because the program was biased against women.
How does that happen? It’s simple: AI is trained on data. When that data has conscious or unconscious bias, the AI will learn from that and incorporate those biases into its algorithm. So any bias present in the data set that AI is trained on can impact the algorithm and lead to hiring biases.
Types of AI Hiring Bias
There are many opportunities for AI to pick up on bias from data and information given to it, or bias from the algorithm itself, which then has a direct impact on hiring decisions that come from AI analysis. Hiring bias when using AI can happen for a number of reasons, such as societal biases within the data, algorithmic influence, or unbalanced data.
Different types of hiring bias that can be present with AI-assisted tools include:
Algorithmic bias: Bias that comes from the algorithm rather than the data, due to factors such as the depth of the neural network or previous information in the algorithm.
Measurement bias: Bias that appears when measurement mistakes or uneven conclusions are made as the dataset is being built, leading to bias against specific demographics represented in the data.
Sample bias: Bias that occurs when AI training data does not accurately reflect real-world measurements or demographics. It can lead to problems such as the over or underrepresentation of certain populations.
Representation bias: Bias which appears during data collection if certain groups are represented unequally. This could include data that doesn’t consider outliers or anomalies, or unequal representation of different demographics.
AI can be biased against anything that it was trained on, but is typically areas such as race, gender, disability status, or ethnicity.
Consequences of AI Hiring Bias
Any type of bias exhibited by an organization, regardless of intent, can be big trouble. Allowing bias in any type of HR process can lead to consequences, especially within the context of recruitment, due to regulations around equal employment.
Not proactively mitigating AI hiring bias can open the company up to risks across the company. Here are just a few of the consequences that can come from hiring bias.
Legal/Ethical Implications
The biggest concern that AI hiring bias can open organizations up to is legal or ethical issues.
Many areas have legislation around discrimination specifically at work, which covers discrimination against individuals based on factors like race, gender, age, and more. Many organizations are required to implement non-discrimination practices, which are meant to protect candidates from being rejected from a position solely based on a part of their identity.
Organizations are responsible for their use of AI, whether the bias was known or unknown by the organization.
Even if an organization isn’t brought into any legal trouble, hiring bias – whether done by AI or not – can be a major ethical concern for HR, and should be treated as one.
Reduced Diversity
If an AI algorithm favors a certain demographic – for example, holds bias against non-white candidates – the organization will therefore be less diverse overall. In addition, if employees are aware of hiring biases, those already within the organization who have demographics that are being discriminated against may leave, too.
Reduced diversity within the organization doesn’t just damage any existing DEI efforts; a less diverse organization can suffer from hindered innovation, creativity, and collaboration. A loss of diverse perspectives can stall an organization’s overall development.
Damage to Company Reputation and Culture
If word gets out about an organization using biased AI in the hiring process it can cause real damage to an organization’s reputation, which can lead to profit loss, higher turnover, and trouble attracting qualified candidates.
This can do damage on the inside of the organization, too. Many employees are invested in organizational values and value cultural alignment. Finding out that the organization is participating in practices that don’t align with individual values can cause disillusionment, low morale, and disengagement.
Missing Out on Quality Talent
AI hiring bias eliminates a whole pool of candidates that could make a positive impact on the organization. Organizations may never even realize the quality of the candidates that have been rejected due to AI hiring bias because there is no human oversight to recognize a top-notch candidate.
This can lead to making a bad-fit hire, which can be costly. It also means that quality talent may bring their skills and innovation to a competitor, putting the entire organization at a disadvantage.
6 Steps to Avoid AI Hiring Bias
It’s clear that AI hiring bias can be bad news for organizations, but it’s not always easy to spot. In fact, many organizations use AI tools because of their potential to reduce bias, but can end up making biased hiring decisions anyway.
To help reduce the risk of AI hiring bias, here are 6 steps an organization can take in the recruiting process.
Assign a Designated AI Overseer
AI should never be left to make a final decision on anything without human oversight. Appoint an employee to oversee any AI efforts within the recruiting process to act as a human fact-checker for anything that the AI produces, especially when it comes to candidate pools. If possible, ask the AI to explain why they chose a particular candidate or rejected a certain candidate to ensure that the AI’s decision was made accurately.
With an oversight team, organizations can ensure that any overlooked candidates were unfairly thrown out and help catch errors before any decisions are made.
Flag and Report Biases
Make it clear to those involved in the recruiting process that any instances of bias or discrimination that they see in the AI software should be immediately flagged and reported. This might involve some education or training for employees to proactively spot AI bias.
Reporting biases doesn’t just help stop one instance of bias; it can help stop the same scenario from happening again further down the line.
Improve Training Data
As mentioned, AI inherits its bias from the data it is trained on. Therefore, if you improve and diversify the data the AI learns from, you can help reduce the chance that the AI will make a biased decision.
For example, AI that’s trained on resumes of primarily white men may have an unfair bias against non-white or female candidates. If that training was expanded to include non-white candidates or women, the AI may not show as much of a preference towards a specific gender or race.
Conduct Audits Regularly
Regularly auditing recruitment AI technology can help keep track of any patterns of bias in the AI and ensure candidates are being assessed fairly. This can also serve as a reference in the case of a bias claim, helping to back up any decisions an AI algorithm made.
It’s also important to conduct audits when AI data is updated to ensure that no new biases pop up in the algorithm.
Choose Fairness-Aware Algorithms
The prevalence of AI bias has led some AI algorithms to begin implementing fairness-aware algorithms, which analyze data with consideration for potential areas of discrimination or bias.
Some AI software also allows users to put in “fairness constraints' ' which can re-weight or re-sample data to reduce bias in the algorithm.
Request and Review Candidate Feedback
Provide opportunities for candidates to leave feedback on the recruiting process. This can help identify and investigate any potential bias claims proactively and, hopefully, address the concern or provide candidates a fair chance at the position.
Best Practices to Ensure Equity
Although AI can leave room for bias or error in decision-making during the recruiting process, it can also provide a variety of benefits to help free up time for recruiters and reduce time-to-hire, whilst ensuring the chosen candidate is the best fit for the organization.
There are ways to help ensure equity while still using AI during recruitment. Here are some best practices to promote fairness in the hiring process:
Provide employee training on bias: Ensure that employees are aware of the possibility for bias and are properly trained on identifying and mitigating bias. Provide this training regularly, and ensure that all new employees are properly trained before getting involved in the AI process.
Establish ethical guidelines: Consider establishing a “code of conduct” for AI use in recruiting. This can help keep everything fair and provide a reference to look back to if there are any concerns about potential bias or issues with the technology.
Disclose the use of AI to candidates: Always disclose the use of AI within the candidate screening process to job applicants. If possible, provide an option for candidates to opt out of automated screenings, which can help eliminate potential issues before they even arise.
Ensure success criteria is equitable: If you can set parameters for what resume criteria to pass the screening process, review the criteria to ensure that it does not promote any type of bias.
Final Thoughts
AI hiring bias can be detrimental to an organization and unfairly reject candidates that might be the perfect fit for the company. However, there are steps that an organization can take to make sure their AI recruiting tools fairly assess candidates. Used correctly, AI can help simplify and streamline the hiring process. However, it’s always important to understand and be on the lookout for potential risks.
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