Is AI Tech the Best Way to Screen Resumes of Quality Engineers?

Tips to Help Secure the Best Engineering Hires

As a recruiter of quality engineers, you or your HR manager must consider how to supercharge the hiring process. Artificial intelligence (AI) could be a technology that helps to streamline and shorten the hiring cycle. Indeed, according to technology group Ideal, 96% of recruiters believe that AI can help enhance talent acquisition and retention.

In this article, we explore whether AI could help you screen resumes more effectively to improve your talent acquisition for quality engineers.

Speeding Up the Hiring Process for Quality Engineers

According to benchmark data from Workable, the average lead time to hire an engineer in the US is 58 days. This is almost two months that your team could be down a quality engineer. Each day costs you time and money.

Reviewing resumes is a time-consuming part of the hiring process. It’s not unusual for a busy HR manager to take weeks to screen all the resumes submitted to your quality engineer job opening.

Utilizing AI can accelerate this process.

Many AI software apps can screen candidates’ resumes in real time. As applications are submitted for your position they are immediately shortlisted if they are a suitable candidate. Some platforms claim that they reduce the lead time to hire by as much as 70%.

Does Speed Come at a Cost?

With AI dismissing many applicants for your quality engineering position before a human gets the chance to look at them, is there a chance a great candidate will fall through the net?

This is a valid concern to have. However, with the huge stream of applicants that many quality engineering positions receive, your HR team may not be able to efficiently look through resumes. On average, a recruiter spends only six seconds reviewing a resume. This creates a huge problem with efficiency that AI can help fix.

Does AI Screening Create Bias?

AI tools are only as good as the data they learn from. It is possible that if you feed an AI tool with historical data from your organization, it may inherit both conscious and unconscious hiring preferences.

For example, it may start to notice incongruous patterns, and preferences that are not relevant to hiring high-quality candidates. An AI tool might notice that the top-performing quality engineers in your organization played soccer in college and then give preference to candidates who play soccer in its screening process!

Another clear danger with AI is that it is possible for it to learn discriminatory behavior. For example, Amazon created a recruitment AI to screen candidates built on a decade of company data. This AI ended up giving clear preference to male candidates over female candidates and had to be scrapped.

Algorithmic Accountability Act

In 2019, the Algorithmic Accountability Act was passed by congress. This act forces companies to review their algorithms for bias. Recruitment algorithms will fall under this law.

This act is designed to cover countless tools and address the issues created by them – thus avoiding another error like Amazon’s. For companies, this act means that if you start to use a recruitment AI, you need to be ready to take 100% accountability for it.

And the consequences for getting it wrong?

Candidates may now sue you if you are using an AI that is producing biased results. The clear issue here is that often you may be unaware until it is too late, and your data set has created a biased algorithm. The cost may not be limited to you missing out on the best candidates – you could be sued for damages and suffer reputational damage, too.

To Sum Up

AI is a great technology. It certainly has the power and potential to help reduce the costs of hiring, slash the time to hire, and remove poor-quality candidates from the process early. However, while the potential benefits of embedding AI into your recruitment process are tempting, there are also many possible drawbacks.

Overall, AI technology is promising for recruiters, and especially in the candidate screening process. But – and this is a big but – it is critical to realize that it is only as good as the data you feed it. A small error in that data could create a very costly mistake.

At MForce we take a more human approach to screening and matching of candidates for jobs for quality engineers and all the positions we are tasked with filling for our clients. To learn more, contact MForce Staffing today.