Spotting and Reducing Potential Bias in AI HR Decisions

28 of April, 2023

It’s no secret that modern HR technology is powered by Artificial Intelligence (AI). AI-driven solutions help organizations make better decisions, faster. However, this same technology is not without its potential pitfalls. One of the biggest concerns is the potential for bias in AI-powered HR decision making.

Bias can be unintentional and difficult to spot, so it’s important for HR managers and business owners to be aware of the signs. Having a clear understanding of what bias looks like and how to reduce it is paramount to creating an inclusive workplace that values diversity and fairness.

Let’s look at some common types of bias to watch out for, plus practical tips on spotting and reducing potential biases when it comes to AI-powered HR decisions.

Why Reducing Bias in AI Is Important for HR Managers

As an HR manager, it’s essential to make sure that your team is making decisions that are fair, unbiased and consistent with your organization’s values. However, this becomes complicated when you’re using AI for HR decision-making.

AI can have potential biases embedded within its algorithms—which can lead to decisions that are unfair or discriminatory. That’s why it’s important for HR managers to understand the potential sources of bias and take steps to identify and reduce them in their AI-powered decisions.

The first step is to ensure that the data used to develop and train the models is accurate and unbiased. This includes scrubbing out any gendered language, ensuring there is a diverse set of data points, and controlling for external factors such as location or industry that could influence the outcome of an AI decision-making process.

Another important step is to assess if the models are being trained in a way that encourages a lack of bias. This includes testing for overgeneralization of data points, checking for any implicit bias resulting from skewed training sets, utilizing techniques such as data anonymization and setting limits on algorithmic parameters to ensure fairness in decision-making.

Understanding Sources of Bias in AI

In order to address potential bias in AI-powered HR decisions, it’s important to understand the sources of potential bias. There are two main categories of bias that can arise in AI decisions: algorithmic and data.

• Algorithmic Bias: This type of bias is a result of the algorithm design; it’s an inherent functionality that can cause an AI system to make incorrect decisions. Algorithmic bias can be caused by a lack of data or too few parameters, resulting in poor generalizations and unintentional discrimination.
• Data Bias: This type of bias occurs when the data used to train an AI system is biased—for example, if a resume data set only includes male names or if job postings skew toward traditionally masculine roles. Data bias can also occur due to disparate impact; hiring practices that appear neutral on the surface but inadvertently screen out certain groups.

By understanding these primary sources of potential AI bias, HR professionals can take steps to reduce the chances of them occurring in their own systems.

Steps to Identify Algorithmic Biases in HR Systems

One of the most important steps in tackling potential bias when it comes to AI-powered HR decisions is to first identify where the biases are coming from. This can be a tricky process, however, as algorithmic bias can be subtle and hard to pinpoint.

Fortunately, there are some steps you can take to get a better understanding of how your AI-powered HR system may be introducing or perpetuating biases in your hiring decisions. Here are three important activities you should incorporate into your process:

Analyzing Data Sources

The data on which AI-powered HR systems is based needs to be critically analyzed for potential sources of bias. This kind of analysis requires examining the data for patterns associated with traits such as race, gender, or age that could create potential disparities in decision making. Once these sources have been identified, they must then be addressed before any decisions can be made.

Rigorously Testing Systems

It’s also important to rigorously test any AI-powered systems used in HR decision making to ensure that they are not unfairly discriminating against any particular group of candidates. Regular testing should occur throughout the entire lifecycle of the system in order to identify and address any emerging issues related to bias.

Having A Human Element In The Process

Integrating human judgment into the decision making process is important in order to ensure fairness and accuracy in the hiring process. Although AI systems can help automate certain aspects of the recruitment process, having humans involved ensures that any potential biases are discovered and addressed before decisions are made.

Best Practices for Reducing AI-Powered Bias

When it comes to reducing bias in AI-powered HR decisions, there are a few best practices you can use to ensure that your decisions are fair and equitable. Here’s a quick rundown of the most important ones:

Establish Ground Rules

First, ensure that all decision makers understand their role in limiting bias. It doesn’t matter if HR decisions are being driven by AI or a human – both need clear ground rules and expectations on how personal biases can be avoided.

Regularly Audit Your Decisions

Companies should develop an audit program to review changes in decisions and potential sources of bias over time. An audit should include data points as well as qualitative measures of compliance with policy initiatives, such as document reviews.

Encourage Diverse Teams

It’s important to have a diverse team assessing AI systems – people from different backgrounds who bring different experiences and perspectives can help reduce the potential for bias. This could mean hiring people with diverse skillsets, such as those with backgrounds in data science and social science, or even consulting semantic experts.

Monitor Outcomes & Evaluate Solutions Regularly

Finally, companies should monitor what happens after the decision is made – how is the outcome affected by the decision? Is there any evidence of bias? Monitoring outcomes allows companies to evaluate whether their solutions are meeting their goals, and to take corrective action if necessary.

Conclusion

When employees engage in AI-powered HR decisions, it’s essential to ensure potential bias is addressed and minimized. We can start by understanding the data that’s being used in the AI decision making process and how bias may be present in it. It’s also important to actively monitor AI decisions to ensure that bias does not creep in.

Organizations seeking to reap the benefits of AI should use a combination of approaches – from data science to policy and process – to build and deploy AI-powered HR decisions that are innovative, ethical, and unbiased.

At Sesame, we’re focused on creating an AI-powered HR platform that helps organizations make unbiased, data-driven decisions. By working together, we can take steps towards reducing potential bias in AI-powered HR decisions and ultimately create an even playing field for all employees.