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A.I. — A Trojan Horse for Hiring?

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.

  • My company always selects the right candidates.
  • We always use 100% objective information when selecting who to interview.
  • People, jobs, and companies don’t change.
  • Past decisions accurately reflect the future goals of our business.

If you disagree with any or all the above statements, then you should think twice before inviting AI through the door.

What is AI

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:

  • Artificial intelligence - AI is a subfield of computer science, that was created in the 1960s, and it was (is) concerned with solving tasks that are easy for humans, but hard for computers. In particular, a so-called Strong AI would be a system that can do anything a human can.
  • Machine learning - The word learning in machine learning means that the algorithms depend on some data, used as a training set, to fine-tune some model or algorithm parameters.
  • Data Science - Covers all industries and fields, but especially digital analytics, search technology, marketing, fraud detection, astronomy, energy, healthcare, social networks, finance, forensics, security (NSA), mobile, telecommunications, weather forecasts, and fraud detection. An important component of data science is automation, machine-to-machine communications, as well as algorithms running non-stop in production mode (sometimes in real time), for instance to detect fraud, predict weather or predict home prices for each home.
  • Deep learning – DL is sometimes referred to as the intersection between machine learning and artificial intelligence. It is about designing algorithms that can make robots intelligent, such as facial recognition techniques used in drones to detect and target terrorists, or pattern recognition / computer vision algorithms to automatically pilot a plane, a train, a boat or a car.
  • Natural language processing - NLP is simply the part of AI that has to do with language (usually written).

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.

Why it’s biased

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.

Why it’s blind to the future

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.

How to proceed

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:

  • What technologies are actually being used to influence my hiring decisions?
  • What artificial intelligence and machine learning approaches are being implemented?
  • What controls do I have over the technologies?
  • What long term effects will this technology have on my organization?

If you’d like to know more about AI and the future impact on your business, please feel free to contact me directly at andrew@vettd.ai

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