Hiring Mistakes That Humans Make and AI Will Never Make (Part 2)

Alon Men

In the previous blog post we started our examination of common hiring mistakes that can be solved with the use of AI.

We discussed how AI can help candidates anonymously express their aspirations, and how AI can enable recruiters to expand their qualification requirements beyond “skills and experience”.

In this article we will continue our examination of additional mistakes and how AI can solve them.

Confirmation Bias 

Let’s face it, humans are bias in their nature. When a recruiter uses the term “gut feeling” to describe her decision regarding a candidate she is, in fact, saying that she is using, what is called, “Confirmation Bias”.

Confirmation bias is when people create a hypothesis in their minds and look for ways to prove it.

When the recruiter says: “I would like to trust my gut feeling on this” she is seeking out confirmation of her preconceived beliefs about a candidate based on, for example, the college they attended, even before the interview.

Some would say that this is an advantage, because the recruiter has accumulated good hiring experiences from this collage. But, others would argue that this practice can result in missing great candidates that may not make it to the interview or be perceived as less competent than others because of these assumptions.

These assumptions can be related to age, race, gender and other factors as well.

 
Introducing AI into the hiring process can solve this challenge. AI can use a much broader data set and analyze many more data points (i.e. candidate factors/parameters) than humans.

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Although AI is often designed to mimic human behavior, and they even sometimes “trained” by a human recruiter, they can also “learn” and “think” very differently from humans.

Various machine learning algorithms have their own “thinking” models, that allow them to produce results that human would not. This different way of “thinking” makes it hard to translate to human terms.

The explanation of how they came up with a certain result can be given only in mathematical or algorithmic terms. When the AI considers all the factors using a mathematical model, it essentially eliminating the confirmation bias. Its matching decision is more “objective” and can yield unexpected, but effective results.

Shooting for the stars 

Recruiters always claim to look for the “best fit'” for a position. But, in reality, most of them try to hire the “stars”, from the best colleges, the biggest corporate names etc.

The problem is that everyone else is chasing those folks, too. So, they’re going to be expensive, they’re going to be unavailable, and often they’re not going to give you the results you need.

For many companies, especially young startups, this is not necessarily the best fit. It’s like trying to recruit Michael Jordan to the Detroit Pistons…

One of the most memorable examples for this argument is the book Moneyball (yes, I know there was also a movie…).

The central premise of Moneyball is that the collective wisdom of baseball insiders (including players, managers, coaches, scouts, and the front office) was subjective and often flawed.

Before sabermetrics (the statistical analysis described in the book) was introduced to baseball, teams were dependent on the skills of their scouts to find and evaluate players.

The book argues that the Oakland A’s’ front office took advantage of more analytical gauges of player performance to field a team that could better compete against richer competitors in Major League Baseball (MLB).

Using statistical analysis, they had demonstrated that on-base percentage and slugging percentage were better indicators of offensive success, and the A’s became convinced that these qualities were cheaper to obtain on the open market than more historically valued qualities such as speed and contact.


I won’t spoil the movie/book to those who haven’t watched/read it, but as you can imagine this was the key to their success.

They were crunching stats to find the cheaper players that can deliver exactly what they needed.

Today this kind of sabermetrics can be applied to tech hiring using AI to analyze a huge data set. In this way companies can find talent that is more affordable, and more tuned to their needs, without shooting for the stars.    

AI and machine learning tech are evolving at a fantastic rate. As we’ve seen they can be a great tool to add to a sourcer’s or recruiter’s toolbox.

By adding AI to the process, sourcers and recruiters can not only speed up the process, they can bypass the hiring mistakes presented throughout this two-part blog post and ensure more relevant results and higher employee retention rates.