Are AI Interviews Discriminating Against Candidates?
Business leaders have been incorporating Artificial Intelligence into their hiring strategies, promising streamlined and fair processes. But is this really the case? Is it possible that the current use of AI in candidate sourcing, screening, and interviewing is not eliminating but actually perpetuating biases? And if that’s what’s really happening, how can we turn this situation around and reduce bias in AI-powered hiring? In this article, we will explore the causes of bias in AI-powered interviews, examine some real-life examples of AI bias in hiring, and suggest 5 ways to ensure that you can integrate AI into your practices while eliminating biases and discrimination.
What Causes Bias In AI-Powered Interviews?
There are many reasons why an AI-powered interview system could make biased assessments about candidates. Let’s explore the most common causes and the type of bias that they result in.
Biased Training Data Causes Historical Bias
The most common cause of bias in AI originates from the data used to train it, as businesses often struggle to thoroughly check it for fairness. When these ingrained inequalities carry over into the system, they can result in historical bias. This refers to persistent biases found in the data that, for example, may cause men to be favored over women.
Flawed Feature Selection Causes Algorithmic Bias
AI systems can be intentionally or unintentionally optimized to place greater focus on traits that are irrelevant to the position. For instance, an interview system designed to maximize new hire retention might favor candidates with continuous employment and penalize those who missed work due to health or family reasons. This phenomenon is called algorithmic bias, and if it goes unnoticed and unaddressed by developers, it can create a pattern that may be repeated and even solidified over time.
Incomplete Data Causes Sample Bias
In addition to having ingrained biases, datasets may also be skewed, containing more information about one group of candidates compared to another. If this is the case, the AI interview system may be more favorable towards those groups for which it has more data. This is known as sample bias and may lead to discrimination during the selection process.
Feedback Loops Cause Confirmation Or Amplification Bias
So, what if your company has a history of favoring extroverted candidates? If this feedback loop is built into your AI interview system, it’s very likely to repeat it, falling into a confirmation bias pattern. However, don’t be surprised if this bias becomes even more pronounced in the system, as AI doesn’t just replicate human biases, but can also exacerbate them, a phenomenon called “amplification bias.”
Lack Of Monitoring Causes Automation Bias
Another type of AI to watch for is automation bias. This occurs when recruiters or HR teams place too much trust in the system. As a result, even if some decisions seem illogical or unfair, they may not investigate the algorithm further. This allows biases to go unchecked and can eventually undermine the fairness and equality of the hiring process.
5 Steps To Reduce Bias In AI Interviews
Based on the causes for biases that we discussed in the previous section, here are some steps you can take to reduce bias in your AI interview system and ensure a fair process for all candidates.
1. Diversify Training Data
Considering that the data used to train the AI interview system heavily influences the structure of the algorithm, this should be your top priority. It is essential that the training datasets are complete and represent a wide range of candidate groups. This means covering various demographics, ethnicities, accents, appearances, and communication styles. The more information the AI system has about each group, the more likely it is to evaluate all candidates for the open position fairly.
2. Reduce Focus On Non-Job-Related Metrics
It is crucial to identify which evaluation criteria are necessary for each open position. This way, you will know how to guide the AI algorithm to make the most appropriate and fair choices during the hiring process. For instance, if you are hiring someone for a customer service role, factors like tone and speed of voice should definitely be considered. However, if you’re adding a new member to your IT team, you might focus more on technical skills rather than such metrics. These distinctions will help you optimize your process and reduce bias in your AI-powered interview system.
3. Provide Alternatives To AI Interviews
Sometimes, no matter how many measures you implement to ensure your AI-powered hiring process is fair and equitable, it still remains inaccessible to some candidates. Specifically, this includes candidates who don’t have access to high-speed internet or quality cameras, or those with disabilities that make it difficult for them to respond as the AI system expects. You should prepare for these situations by offering candidates invited to an AI interview alternative options. This could involve written interviews or a face-to-face interview with a member of the HR team; of course, only if there is a valid reason or if the AI system has unfairly disqualified them.
4. Ensure Human Oversight
Perhaps the most foolproof way to reduce bias in your AI-powered interviews is to not let them handle the entire process. It’s best to use AI for early screening and perhaps the first round of interviews, and once you have a shortlist of candidates, you can transfer the process to your human team of recruiters. This approach significantly reduces their workload while maintaining essential human oversight. Combining AI’s capabilities with your internal team ensures the system functions as intended. Specifically, if the AI system advances candidates to the next stage who lack the necessary skills, this will prompt the design team to reassess whether their evaluation criteria are being properly followed.
5. Audit Regularly
The final step to reducing bias in AI-powered interviews is to conduct frequent bias checks. This means you don’t wait for a red flag or a complaint email before taking action. Instead, you are being proactive by using bias detection tools to identify and eliminate disparities in AI scoring. One approach is to establish fairness metrics that must be met, such as demographic parity, which ensures different demographic groups are considered equally. Another method is adversarial testing, where flawed data is deliberately fed into the system to evaluate its response. These tests and audits can be carried out internally if you have an AI design team, or you can partner with an external organization.
Achieving Success By Reducing Bias In AI-Powered Hiring
Integrating Artificial Intelligence into your hiring process, and particularly during interviews, can significantly benefit your company. However, you can’t ignore the potential risks of misusing AI. If you fail to optimize and audit your AI-powered systems, you risk creating a biased hiring process that can alienate candidates, keep you from accessing top talent, and damage your company’s reputation. It is essential to take measures to reduce bias in AI-powered interviews, especially since instances of discrimination and unfair scoring are more common than we might realize. Follow the tips we shared in this article to learn how to harness the power of AI to find the best talent for your organization without compromising on equality and fairness.