AI meets HR in an AI-driven workforce

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AI meets HR in an AI-driven workforce

Have your clients started asking about AI-powered platforms to enhance their HR processes? Of the many fields where AI is gaining momentum, HR seems to be moving up the list. I’m all for improving how HR folks go about their roles, but I wanted to know a little more about the potential for bias in HR-related AI models.

I welcomed Michael Campion, the Chief Science Officer at Scoutr, onto a bonus episode of The Business of Tech to walk me through the state of AI in HR. He’s particularly knowledgeable about deploying skills-based models to analyze employees’ workplace potential, so keep reading for an in-depth look at the pros and cons of an on-the-ground AI use case.

AI in HR: the Pros

First thing first: what does a Chief Science Officer do? In Campion’s case, he’s in charge of working with Scoutr’s CEO and CTO to make sure their vision for the company’s creations is aligned with the latest science.

In other words, Campion is the exact type of person who actually knows what’s going on behind the scenes with these AI tools. With that expertise in mind, I asked Campion to share what kind of pros we’re seeing on the HR AI front.

The answer, surprisingly, has to do with expanding the human skillset. At Scoutr, Campion and his team have created a skills-based AI model that takes a word2vec machine learning program and multiple data sources to better understand the interrelatedness of skills. They then create skills libraries for organizations that help HR folks assess someone’s potential for certain types of jobs.

In his words:

“It creates knowledge workers, so people that typically were doing stuff that did not involve a lot of knowledge before now do. And it makes analysts out of people, which is really exciting, and it really increases the satisfaction associated with their jobs.”

Who wouldn’t see that as a pro?

AI in HR: the Cons

If you’re wary of that premise, you’re not alone. I asked Campion what the downsides are to this type of use case, and he explained that employee attitudes can take a hit with tools like this. In his experience, some people who are asked to participate in AI assessments like Scoutr’s don’t like the lack of human involvement – though people help make the models, they’re not needed to actually execute the assessments.

On top of that emotional resistance, Scoutr faces the same problem many other technologies do: a large-scale change across entire organizations is never easy. If people aren’t ready for it, implementing HR AI is nothing short of tricky.

And, of course, AI needs strong data to generate strong results. Without a reliable model, organizations won’t get the benefit they’re looking for.

The Replacement Question

Let’s zero in for a moment on the lack of human involvement in AI-driven HR assessments. Does that mean Scoutr is replacing people? I double-checked with Campion, and his answer is what you’d hope – no, employees can’t be replaced by this type of AI (at least not now). They may not execute the assessments, but someone has to create the model.

This confirms my own belief about the future of AI in the workplace: it’s not about replacing people, it’s about augmenting their work. In this case, it’s the talent scout getting better information to help them achieve better screenings.

Furthermore, Scoutr itself is designed to help people upskill and reskill to transform their roles. By finding connections between skill sets, employees can uncover new career pathways they might never have considered.

Minimizing Bias

As we all know by now, the bias that’s inherently built into databases ends up built into LLMs themselves. So how can organizations ensure their AIs and models are as free from bias and discrimination as possible?

I ran this by Campion, and his answer is simple: audits. For a tool like Scoutr, it’s critical to look at protected classes and see if scores have associations. Skill assessments are the main area of HR where AI is coming into play, so he believes this applies to anyone working in this overlap.

In an ideal world, you should be running these audits both internally and externally. Large organizations that might be prone to litigation should especially consider inviting external auditors.

That’s all for people developing models in-house, but what about people who are leveraging off-the-shelf products with built-in LLMs? How can they control bias?

Most smaller organizations wouldn’t be able to afford external audits, creating an opportunity for service providers to step in and help out. I asked Campion if that’s reasonable, and he confirmed that’d be a great place to jump in.

Misconceptions & Predictions

I also gave Campion the space to push back against any misconceptions he’s heard about AI in HR. His answer was two-fold.

First, AI isn’t actually being used in HR as much as people think. We’re still on a path to understanding how to optimize it, meaning we’re still in the very early days of its usage.

Second, there’s a good amount of research (according to Campion) that AI can actually understand the dimensions of personality better than humans can, especially in relation to workplace performance. One reason for this is that it can predict numerous outcomes simultaneously – something human judgment struggles with.

So, where will the technology evolve from here? I asked Campion for any predictions, and he mainly thinks that a lot of HR tech will consolidate in what Scoutr calls ‘talent marketplaces that incorporate AI.’

For the workers themselves, he thinks this massive reskilling and upskilling will transform more people into analysts.

“Usually, you’re using skills themselves and as sort of a one-to-one match. But isn’t it interesting to think about the notion of the relatedness of skills?” he said.

Where to Start

For service providers or business owners who want to implement this form of AI, Campion has some advice.

First, shop around. Not all vendors who claim to use AI are actually using the models you have in mind. Second, focus on the attitude and reactions of end users, including the employees and applicants being assessed.

If you’d like to learn more about Campion and his work at Scoutr, head to www.scoutr.team or reach out directly at [email protected].

Do you have experience in the HR AI space? As always, I’m available for insight, questions, musings, and more.

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