4-Steps to Improving Pre-Hire Assessments with AI

Author: Assaf Bar-Moshe, PhD, Head of Research and Development

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Hiring teams are already using different types of communication assessment technology to help them make decisions about which new candidates to hire. AI has the power to augment and streamline existing processes in ways that are beneficial to recruiters and talent acquisition specialists. However, many people fear the term AI because they think it’s going to replace human decision making or human faculties, which is not what we are talking about in the context of hiring assessments. Instead, we’re simply talking about making things more efficient. 

Within a recruitment pipeline, it can take months to hire before you actually put somebody into a position. AI let’s hiring teams speed things up by reducing the time that it takes for a candidate’s communication skills to be evaluated. There are a number of other benefits. Let’s take a closer look at exactly how AI is improving hiring assessments.

1 AI Reduces the Bias of Individuals

Bias is a big issue not only in the AI world but also when assessments are evaluated by humans. From this point of view, AI actually reduces bias. AI is blind to accents, the color of the person, the gender of the person, their age and other features. As data scientists, we do not introduce these features into the machine, so the machine does not use them for classification and therefore we don’t look at a group of women and men separately for example. We don't measure native speakers and non-native speakers separately. The machine makes decisions without knowing if somebody’s a man, is white or anything like that. 

When a test is evaluated by a human, they introduce bias into their evaluation by nature. As much as they would like to, humans cannot entirely isolate their prejudices. This is more pronounced if the team has a large volume of assessments, which would not be evaluated by a single person. Having different people performing evaluations, means different opinions and different skills will be introduced into the process. No matter how you try to standardize the “rubric” or the evaluation process, there will be bias that gets into the evaluation process when it’s done by humans. Humans can have different opinions about the world around them, and they will possess different soft skills. They will also have a bias towards different accents or a different tolerance towards grammatical mistakes, and so on.

2 Use AI to Enhance What Can Be Evaluated

AI enables an evaluation of dozens or hundreds of different features. So if we are evaluating communication skills like grammar or fluency, there are dozens or hundreds of features analyzed at the same time, something that is very difficult for a human to do.

“Humans can say the same thing in many different ways. Some of them are unpredictable.”

Humans can say the same thing in many different ways. Some of them are unpredictable. As such, if they were not modeled before, they wouldn't be evaluated. Using machine learning, however, the models are more flexible and allow us to account for things even if they were not modeled in so many words before.

So with the power of AI, we always learn, and we can build dynamic evaluation processes. Rather than a standardized rubric, by which our assessments are evaluated, AI allows teams to have rubrics that get refreshed and updated on the go, all the time. If new material comes in, the machine learns from this new material and becomes smarter. Rather than being static and saying “this grammar mistake is very bad”, it might be the case that with time, the machine learns that it is not that severe. So we have the ability with AI to maintain a dynamic system of evaluation, rather than a static one that can’t evolve with time.

“AI allows teams to have rubrics that get refreshed and updated on the go”

3 Use AI to Enhance the Format of the Evaluation

Many communication assessments are closed-ended, which flattens the candidate's abilities and opens the door for cheating. Using a dynamic AI system allows hiring teams to construct assessments with open ended questions, where the candidate needs to react to a given scenario in their own words in writing or speaking. 

“A multiple choice format is not AI.”

A multiple choice format is not AI. This is just straight forward true or false. So the only way that an assessment can truly be AI-driven is if you have open-ended questions. 

Instead of measuring one right or wrong response, AI measures the way things are said, how they are said, grammar, fluency, how relevant the answer is to the question that was asked, whether the candidate showed empathy to the customer, used proper language, and more. It’s even better if the open-ended questions actually mimic real on-the-job scenarios. Regardless, with open-ended questions, there is no right answer, and therefore it’s much more difficult to cheat. 

“with open-ended questions, there is no right answer, and therefore it’s much more difficult to cheat.”

4 Use AI to Get Statistically Significant Data Around Your Performance 

With AI, there is no limit to the number of assessments we can evaluate at the same time. This scales up and yields a lot of data over time, which can be used to improve the reliability and the accuracy of the assessment, eliminating false positives with time. 

With several years of experience, we were able to get to a point in which we basically have almost zero false positives in the HiringBranch assessment, meaning no unqualified people passed the test. We have very few exceptions if any. So hiring teams actually become more confident about their performance as well knowing that people who passed the test will be doing a good job. 

With enough data and confidence in the data, management teams can use it to make decisions about the business, like which location candidates perform better, or which hiring partner brings more revenue etc. Using AI to process large amounts of data also means decision-making can happen faster. 

As a scientist, I’m excited by the potential of AI that we’re already seeing within the recruiting and talent acquisition space. It’s certainly increased the hiring confidence of our customers. If you’re thinking about adopting AI in your hiring pipeline, just be cautious, do your research, and verify that what the service provider sells works as proven by data and analysis. And when it does, just embrace it. It will save so much time and effort while giving you unbiased results and more qualified candidates.

Image Credits

Feature Image: Unsplash/Jake Hills

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