Background checks are conducted for a variety of reasons. For example, recruiters do them on potential applicants to verify the accuracy of a candidate’s resume and ensure that they are a good fit for the job.

Others have met someone online who they’d like to pursue a relationship with and want to confirm they’re who they say they are

Whatever the reason, it’s becoming more commonplace than ever to run a background check on another person. Most of these are conducted online due to the ease of access.

However, with the rise of artificial intelligence (AI) and machine learning (ML) technology, background checks are becoming more sophisticated and accurate. This begs the question, is AI making background checks too smart?

How is AI Involved?

AI Making Background Checks Too Smart

Traditionally, background checks involved manually collecting and verifying information from a person directly, online public and court records, and social media profiles. However, with the advent of AI technology, background checks can now be conducted more efficiently and accurately.

AI algorithms can analyze a wide range of data sources, not limited to social media profiles, criminal records, and credit reports. This information is then used to create a profile of the individual that can be used to predict their behavior and suitability for a particular activity, such as employment or renting an apartment.

The AI algorithms use a process known as “data modeling” to identify patterns in the data relevant to the background check. For example, suppose a background check is being conducted for employment. In that case, the AI algorithm might look for repeated actions in the individual’s work history or education that indicate their ability to perform a job.

The AI algorithms can also use “predictive modeling” to forecast an individual’s behavior based on the data analyzed. For example, an AI algorithm might predict that an individual will likely be a reliable employee based on their past work history and references.

What are the Concerns?

AI Making Background Checks Too Smart

While AI can potentially improve the accuracy of background checks, it also raises privacy concerns. Using social media profiles and other personal information highlights issues about data security.

One concern with the use of AI in background checks is the potential for discrimination and bias. If AI algorithms are trained on biased data, they can misreport and even expand on existing biases in the data. This can result in individuals from certain groups being unfairly screened out or excluded from certain activities.

A final yet alarming concern is the potential for AI to get it wrong. While AI algorithms can analyze large amounts of data quickly, they can also make errors in their predictions. For example, an algorithm may mistake a person with the same name and date of birth as another person and provide completely incorrect results.

What are the Solutions?

AI Making Background Checks Too Smart

One potential solution to these issues is to ensure that AI algorithms are transparent and accountable. This means that the algorithms should be designed to be explainable so that users can understand how they work and why they are delivering specific results.

Another solution is to limit the types of data that can be used in background checks. For example, employers could be prohibited from using social media profiles or credit reports in their background checks, as these data sources can be highly personal and potentially biased.

Instead, employers could focus on more objective sources of data, such as criminal records and employment history.

It is also essential to ensure that AI algorithms are designed to be inclusive and unbiased. This means that the algorithms should be trained on a diverse range of data so that they do not discriminate against certain groups of people. Additionally, the algorithms should be regularly audited and updated to remain fair and accurate.

AI Is Getting Smarter, But…

AI can not only conduct background checks faster and more efficiently, but it can also improve the accuracy of the results. However, AI’s involvement also raises concerns about privacy, discrimination, and accuracy.

To address these concerns, it is vital to ensure that AI algorithms are transparent, accountable, and inclusive.

By taking these steps, we can help to mitigate concerns around privacy, discrimination, and accuracy.

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