Do AI interviews discriminate against candidates?
Business leaders incorporate artificial intelligence into their employment strategies and commit to streamlined, fair processes. But is this really true? Is there a possibility that, rather than eliminating the current use of AI in candidate procurement, screening, and interviews, actually perpetuating bias? And if that’s really what’s happening, how can you change this situation and reduce the bias in AI-powered employment? In this article, we investigate the causes of bias in AI-powered interviews, explore real-world examples of AI bias in employment, and propose five ways to enable integration of AI into practice while eliminating bias and discrimination.
What causes bias in AI-equipped interviews?
There are many reasons why an AI-powered interview system can provide biased evaluations of candidates. Let’s explore the most common causes and the types of bias that they cause.
Bias training data causes historical bias
The most common cause of AI bias comes from the data used to train companies, as they often struggle to thoroughly check fairness. If these ingrained inequality are carried over into the system, it can lead to historical bias. This refers to persistent biases in data that men may be more favored than women, for example.
Faulty feature selection causes algorithm bias
AI systems can be optimized intentionally or unintentionally to focus on properties that are unrelated to position. For example, interview systems designed to maximize new recruiting retention can support candidates with ongoing employment and punish those who missed work for health or family reasons. This phenomenon is called algorithm bias and can create patterns that are repeated and solidified over time if they are not addressed without the developer’s realisation.
Incomplete data causes sample bias
In addition to having a staining bias, the data set may also be skewed, and we include detailed information about one candidate group compared to another. In this case, the AI interview system could have a greater advantage for groups with more data. This is known as sample bias and can lead to discrimination during the selection process.
Feedback loops cause acknowledgement or amplification bias
So, what if your company has a history of supporting extroverted candidates? If this feedback loop is built into an AI interview system, it is highly likely that it will be classified as a confirmation bias pattern. However, don’t be surprised if this bias becomes even more pronounced in the system, as AI can not only replicate human bias, but also exacerbate what is called “amplification bias.”
Lack of monitoring causes automation bias
Another type of AI to watch out for is automation bias. This happens when the recruiter or HR team has too much trust in the system. As a result, even if some decisions appear to be illogical or unfair, the algorithm may not be further investigated. This allows bias to be unchecked and ultimately undermine fairness and equality in the employment process.
Five steps to reduce AI interview bias
Based on the source of bias 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. Diversification of training data
This must be a top priority given that the data used to train AI interview systems has a significant impact on the structure of the algorithm. It is essential that the training dataset is complete and represent a wide range of candidate groups. This means covering a variety of demographics, ethnicities, accents, appearances and communication styles. The more information an AI system has about each group, the more likely it is to evaluate all candidates in open positions fairly.
2. Focus on job-related metrics
It is important to identify the required evaluation criteria for each open position. In this way, you will be able to guide your AI algorithms to know how to make the most appropriate and fair choices during the recruitment process. For example, if you are hiring someone for a customer service role, factors such as sound tone and speed should definitely be considered. However, if you add new members to your IT team, you can focus more on technical skills rather than on such metrics. These distinctions help optimize the process and reduce bias in AI-powered interview systems.
3. Provide alternatives to AI interviews
Regardless of the number of measures implemented to ensure that AI-powered employment processes are fair and equitable, some candidates are still not accessible. Specifically, this includes candidates who have no access to high-speed internet or high-quality cameras, or cameras with impairments that make it difficult for AI systems to respond as expected. You should prepare for these situations by providing candidates invited to alternative options for AI interviews. This includes written interviews and face-to-face interviews with members of the HR team. Of course, only if there is a good reason or if the AI system is unfairly disqualified.
4. Check human surveillance
Perhaps the most complete way to reduce bias in an AI-powered interview is to not let the entire process take care of. It is best to use AI for early screening and perhaps for the first round of interviews. Once you have a final candidate, you can transfer the process to a team of human recruiters. This approach significantly reduces workload while maintaining essential human monitoring. Combining AI capabilities with internal teams ensures that your system works as intended. Specifically, if the AI system takes the candidate to the next stage where the required skills are lacking, this will encourage the design team to reevaluate whether the criteria are properly adhered to.
5. Periodic audits
The final step to reducing bias in AI-powered interviews is to perform frequent bias checks. This means you don’t wait for a red flag or email of complaints before taking action. Instead, they become aggressive by using bias detection tools to identify and eliminate AI scoring disparities. One approach is to establish fairness metrics that must be met, such as demographic parity. Another method is hostile testing, where defective data is intentionally fed into the system to evaluate its response. These tests and audits can be performed internally if you have an AI design team or if you can partner with an external organization.
Achieve success by reducing bias in AI-powered employment
Integrating artificial intelligence during the hiring process, especially during interviews, can bring great benefits to the company. However, the potential risk of misusing AI cannot be ignored. If AI-powered systems cannot be optimized and audited, there is the risk of creating biased employment processes that alienate candidates, access to the best talent, and not damage the company’s reputation. It is essential to take steps to reduce bias in AI-powered interviews, especially as cases of discrimination and unfair scoring are more common than we recognize. Follow the tips we share in this article to learn how to leverage the power of AI to find the best talent for your organization without compromising on equality and equity.
