There’s a lot of buzz around artificial intelligence (AI) and machine learning (ML) in talent acquisition right now. It’s important to understand what these technologies are, how they’re being applied, and what impact they may have on the long-term health of your organization.
There’s a lot of buzz around artificial intelligence (AI) and machine learning (ML) in talent acquisition right now. It’s important to understand what these technologies are, how they’re being applied, and what impact they may have on the long-term health of your organization. There are many different ways to apply AI and even more ways that marketers choose to talk about it. To help frame this discussion, consider whether you agree or disagree with the following statements.
If you disagree with any or all the above statements, then you should think twice before inviting AI through the door.
To begin understanding the implications of this technology, let’s take a look at some of the terminology being used to describe it. Keep in mind that the mainstream application of this technology is fairly new to hiring and marketers are heavily experimenting with the language they’re using at your expense.
Definitions you should know:
The usage of this terminology and the implementation of these technologies varies, but the promise of all these tools in talent acquisition is the same: save time and get better candidates.
All commercially available talent acquisitions tools today appear to have settled on the less sophisticated ML-based approach whereby you and your colleagues review candidates and provide ratings. Over time, the machine learns your preferences and presents you with candidates that match those preferences. Think Pandora radio stations and their thumbs up / thumbs down system. For this reason, we will label this application of ML as our Trojan Horse and the one to watch out for.
This type of ML needs to be trained by a human. If you think it will improve your candidate selection or remove bias, you’re wrong. It’s built to learn from you and replicate your actions. In fact, a recent Princeton study proved that machines absorb our biases. The researchers also highlighted that at least humans (unlike machines) are somewhat aware of their biases and able to combat them.
These solutions fall short based on their very definition. We don’t want our AI hiring assistant to be as good as us at selecting the right candidates. We want it to be better. We want it to identify hidden talent and to educate us on the patterns we might not be seeing.
Hiring is an inherently forward looking art. Many dynamic factors influence hiring decisions as people, jobs, companies, and industries are constantly changing. ML applied as discussed uses the summation of your previous decisions to help determine future decisions with no regard for the bigger picture. This methodology lacks the appropriate context needed to hire the individuals that will help the company achieve its goals.
You might get some time savings after you’re done training these machine learning algorithms, but the long term implications of this approach are largely unknown and need to be carefully considered before adoption. As a guide, consider the following questions when shopping for any new hiring technologies:
If you’d like to know more about AI and the future impact on your business, please feel free to contact me directly at firstname.lastname@example.org
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