Speaker 1: Welcome back to on the Job. I'm Avery Thompson. If
Speaker 1: you've applied for a job recently, chances are some filtering
Speaker 1: software saw your resume before any human did. That is,
Speaker 1: if a human saw it at all. Today we're exploring
Speaker 1: how artificial intelligence is transforming how and who we hire.
Speaker 1: And I've got not one, but two experts in the
Speaker 1: field to help us separate fact from fiction on what
Speaker 1: happens after we hit submit.
Speaker 2: My nice being e Banks. I'm the CEO and head
Speaker 2: of Research for Lighthouse Research and Advisory, also the author
Speaker 2: of Artificial Intelligence for HR, among other books.
Speaker 3: So Peter Capelli, I am the George W. Taylor Professor
Speaker 3: of Management. I also direct the Center for Human Resources
Speaker 3: at the Wharton School.
Speaker 1: So, whether you're on the job hunt or the one
Speaker 1: doing the hiring, or like me, just curious what all
Speaker 1: this AI hubbub is about. Stick around. When you ask
Speaker 1: most little kids what they want to do when they
Speaker 1: grow up, you tend to hear some familiar answers, things
Speaker 1: like astronaut, fireman, professional athlete. That wasn't the case with
Speaker 1: Ben Eubanks.
Speaker 2: I wanted to be an HR and as a kid,
Speaker 2: so I watched my parents working in their small business.
Speaker 2: They had trouble hiring people, mainteninent benefits, keeping them long term,
Speaker 2: all those things, and I thought, I'll figure out to
Speaker 2: do that thing, whatever that is.
Speaker 1: For Peter Capelli, well, human resources wasn't exactly his childhood dream.
Speaker 1: Like Ben, he also recognized a system that needed fixing.
Speaker 3: It was kind of the discovery of just how bad
Speaker 3: it was.
Speaker 1: That discovery led Peter to a career in academia. And
Speaker 1: the more he came to learn about how we hire
Speaker 1: in this country, the worse it seemed. To get.
Speaker 3: What we're looking for. I think as researchers is something
Speaker 3: which is a surprise. That's the big win for us.
Speaker 3: And this was a surprise, just unfortunately a band surprise,
Speaker 3: but surprising nonetheless.
Speaker 1: And I think it's fair to say Ben agrees the
Speaker 1: hiring process has always had issues always to that end,
Speaker 1: AI is being heralded as a potential game changer in
Speaker 1: the field, able to not only process thousands of applications
Speaker 1: and seconds, but also uncover relationships and patterns and data
Speaker 1: that humans likely miss and Ben Eubanks says that initially
Speaker 1: most people in the field looked at the arrival of
Speaker 1: AI with open arms.
Speaker 2: There are some opportunities where AI can play and let
Speaker 2: us do things we could have never done unless we
Speaker 2: had an unlimited budget and unlimited people on the recruiting team.
Speaker 2: There's not a company out there like that. So the
Speaker 2: cause of that, everybody's trying to get an infinite amount
Speaker 2: of work done with a finite amount of people hours.
Speaker 1: But that doesn't mean these companies necessarily knew what they
Speaker 1: were working with.
Speaker 2: I don't expect HR professionals, recruiters, learning leaders. I don't
Speaker 2: expect any of them to be experts in technology and
Speaker 2: data science and AI. I don't expect that because they
Speaker 2: have a full time job already.
Speaker 1: And Peter Capelli says, CEOs, we're guilty as anyone forgetting
Speaker 1: caught up in the AI hype.
Speaker 3: The people at the very top of the companies. These
Speaker 3: are the CEOs in particular. I think that large language
Speaker 3: models and generative AI are just plug and play things.
Speaker 3: You just asked chat gbtwo to do your job for you,
Speaker 3: and then they're going to do it right. And as
Speaker 3: a result, they are pushing lower down in HR in
Speaker 3: particular to use more and more AI.
Speaker 1: This plug and play approach. As Peter A. Capelli calls.
Speaker 1: It is something Ben Ubanks is all too familiar with,
Speaker 1: as human resources personnel try to outsource a lot of
Speaker 1: their work.
Speaker 2: Everybody says, letay, I do the work that's kind of repetitive,
Speaker 2: and we can put the humans in on the strategic stuff.
Speaker 2: We can focus on the higher value work. That's the
Speaker 2: big promise of it is we're doing a lot of things.
Speaker 2: There's not a single person ever met that got into
Speaker 2: the worldoking resources like I can't wait to look at
Speaker 2: a stack of resumes, or I can't wait to go
Speaker 2: and schedule interviews and then have to schedule inities, and
Speaker 2: then that manager is six and then with the reschedule like,
Speaker 2: oh gosh, no, they'll pull your hair out. So no
Speaker 2: one loves those things. Those are all that the promises.
Speaker 2: Is not doing those things anymore.
Speaker 1: And while AI can sort out that stack of resumes
Speaker 1: in the blink of an eye, it's becoming clear that
Speaker 1: these systems aren't always doing a good job.
Speaker 3: And the thing that was most prevalent was discrimination. A
Speaker 3: fair amount of evidence that women and also minorities of
Speaker 3: various kinds got systematically lower scores on performance appraisals.
Speaker 2: There could be a potential bias in there, and so
Speaker 2: when I'm seeing that the best companies do their buildings,
Speaker 2: technology is thinking about that, planning for it. But that
Speaker 2: doesn't completely mitigate that risk.
Speaker 1: It's no surprise that both Ben and Peter brought up biases,
Speaker 1: because when it comes to hiring, that's always been the
Speaker 1: elephant in the room.
Speaker 3: We are just full of biases, and particularly if you've
Speaker 3: got hiring managers who are not trained. You know, one
Speaker 3: of the dumber things we've done as a business community
Speaker 3: employer community is that we've cut back on recruiters, who
Speaker 3: are people who do this for living, you know, hiring
Speaker 3: find people, assess them, and we've pushed those tasks onto
Speaker 3: line managers, who are people who don't do this for
Speaker 3: a living and they've got other full time jobs.
Speaker 1: No matter how equitable and objective a company may strive
Speaker 1: to be, humans are inherently prone to biases. So it
Speaker 1: makes sense that some in the HR field saw AI
Speaker 1: as a potential silver bullet, a purely analytical tool that
Speaker 1: could bypass all of our human foibles. No longer would
Speaker 1: we need to rely on gut feelings. Instead, we can
Speaker 1: just press a button and let the numbers speak for
Speaker 1: themselves that there's.
Speaker 2: Some pretty wild data, pretty compelling that's coming out. There's
Speaker 2: an experiment done with recruiters that shows that when they're
Speaker 2: using algorithms, if they think are very predictive, they're more
Speaker 2: likely to take their hands off the wheel and let
Speaker 2: the car drive itself. And we generally know that it's
Speaker 2: not a good idea.
Speaker 1: And unfortunately, as Peter Capelli explains, these AI systems were
Speaker 1: built by humans and rely on historical data that inevitably
Speaker 1: reflects the very inequalities we're trying to move away from.
Speaker 3: There is a particular problem that is unique to large
Speaker 3: language models and machine learning in particular for hiring. The
Speaker 3: data that you're using to train the model is historical, right,
Speaker 3: it can't all happen today, and anything strange going on
Speaker 3: in the labor market ten years ago will show up
Speaker 3: being built into the model. And the thing that was
Speaker 3: most prevalent was discrimination.
Speaker 1: As an example, imagine a company builds a screening system
Speaker 1: to identify the best applicants, only to realize that system
Speaker 1: was filtering out all the women who applied. Unfortunately, that's
Speaker 1: not science fiction, but what actually happened. And at Amazon
Speaker 1: back in twenty fifteen.
Speaker 3: And even if you took out names and other attributes,
Speaker 3: it would still find in the algorithm anything that was
Speaker 3: associated with being a woman, like your college courses. You know,
Speaker 3: if you took women's studies courses, more likely be a
Speaker 3: woman maybe, And so it would still give women lower
Speaker 3: scores because historically that was one of the predictors. Any
Speaker 3: biases that were in the data earlier are going to
Speaker 3: be codified into the algorithm.
Speaker 1: Not sounding like the answer to all our hiring problems now,
Speaker 1: is it? In fairness, though, these are the growing pains
Speaker 1: of a new technology, one whose powers and limitations were
Speaker 1: still discovering.
Speaker 2: See, AI is not a decision engine. It's a recommendation engine,
Speaker 2: especially in hiring it should be, and so it's recommending
Speaker 2: this path, and it's giving me inputs, and I, as
Speaker 2: the HR person or the recruiter, can make a choice
Speaker 2: on what to do next.
Speaker 1: But companies aren't the only ones trying to utilize the
Speaker 1: power of AI. Job applicants are also giving these new
Speaker 1: technologies to go. So when we come back from the break,
Speaker 1: we'll explore how AI is helping job seekers land their
Speaker 1: next gig.
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Speaker 1: We're back with our human resources experts, Ben Ubanks and
Speaker 1: Peter Cappelli. In the first half of the episode, we
Speaker 1: uncovered how HR departments and staffing agencies are incorporating AI
Speaker 1: into their hiring processes. But they aren't the only ones
Speaker 1: utilizing this powerful new tool. As companies increasingly automated hiring,
Speaker 1: job seekers are also turning to AI.
Speaker 2: We're seeing in our research one out of every three
Speaker 2: candidate says they're either using AI to help apply for
Speaker 2: jobs or they're going to start. So it becomes almost
Speaker 2: an arms race. If everybody else is doing it and
Speaker 2: I'm not, I'll be shut out the market.
Speaker 1: But can you blame these applicants. Anyone who's ever applied
Speaker 1: for a job knows it's a tedious, time consuming process
Speaker 1: and often hitting submit feels like we're just throwing our
Speaker 1: resumes into a digital waste paper basket. And unfortunately, there's
Speaker 1: actually a fair bit of data to back this up.
Speaker 3: A couple of years ago. The last time I look
Speaker 3: the probability that you're going to get hired if you
Speaker 3: send your application into one of the big job where
Speaker 3: it's like Indeed or one of those, that was about
Speaker 3: two percent. So how many people would actually look at it, say,
Speaker 3: the short list is double that, so maybe you got
Speaker 3: a four percent chance. Don't expect that people are sitting
Speaker 3: around at night debating the merits of your resume.
Speaker 1: It seems that job seekers have responded to these incredibly
Speaker 1: low success rates by flooding the market. Would you, at
Speaker 1: least in theory makes sense.
Speaker 2: Let's say I'm tired of applying for jobs. I can
Speaker 2: go to a lazy apply or some other websites I
Speaker 2: can pay a little fee, and they're automatically applying to
Speaker 2: hundreds or thousands of jobs on my behalf without me
Speaker 2: ever lifting another finger to do that. That's happening right now,
Speaker 2: and employers are getting flooded by resumes because of this.
Speaker 2: I talked to a company the other day that said we
Speaker 2: used to get twenty thousand resumes a month. Big global company.
Speaker 2: Now we're getting twenty thousand resumes every two and a
Speaker 2: half days.
Speaker 1: So, Peter, are we at a state where it's just
Speaker 1: computers applying to computers? Yeah?
Speaker 3: I think we probably are now. I guess the slight
Speaker 3: caveat to that is it has to start and be
Speaker 3: based on something real. That is, you would think any
Speaker 3: smart employer, their selection software is ba them what they
Speaker 3: really want. And if you're an applicant, your experience really
Speaker 3: is a little unique. So it isn't completely standardized at
Speaker 3: least if it's sensible. But is it automated? Still? Yeah,
Speaker 3: it is.
Speaker 1: I gotta say I find it refreshing to hear two
Speaker 1: people more knowledgeable about AI not talking to me about
Speaker 1: it like a couple of used car salesmen. Sure, AI
Speaker 1: will probably transform things in our world, including the way
Speaker 1: we hire, but it doesn't seem like it's going to
Speaker 1: happen overnight. In the meanwhile, Ben Ubanks and Peter Cappelli
Speaker 1: suggest companies take a more cautious and considerate approach to
Speaker 1: how they incorporate these technologies.
Speaker 2: Well, I'm doing a keynote on this topic of AI.
Speaker 2: The headline in the Guardian says, job hunting and the
Speaker 2: age of AI is grim, frustrating, dehumanizing. When I ask
Speaker 2: an audience of HR leaders, hey, how many of you
Speaker 2: put your hands up if that's what you want your
Speaker 2: job start process to look like at your company? Not
Speaker 2: a single hand goes up, Absolutely not. But that's what
Speaker 2: we end up with if we're using AI because someone
Speaker 2: told us to quote unquote get to an AI.
Speaker 1: And we need to remember that these advanced systems are
Speaker 1: only as good as the information and we're feeding them.
Speaker 1: So before we look to them for the answers to
Speaker 1: all of our future problems, we might want to first
Speaker 1: look back at how we fall in short in our
Speaker 1: own past.
Speaker 2: Every company out there that takes a job description out
Speaker 2: of the old vault blows all the dust off and says, Okay,
Speaker 2: let's use the same one from five years ago to
Speaker 2: hire this job again. You are doing your company to
Speaker 2: service because that job has changed. I can guarantee you
Speaker 2: it has changed to some degree. It's an imperfect system, Honestly, Avery,
Speaker 2: we're using imperfect job descriptions with imperfect resumes and trying
Speaker 2: to make a mess out of that. It's like getting
Speaker 2: two boxes of puzzle pieces mixing all together and try
Speaker 2: to make one picture, and it's really hard to do.
Speaker 3: What happens now, for the most part, is that we
Speaker 3: hire people who we like, and those tend to be
Speaker 3: people who are similar to us.
Speaker 1: So, after all this discussion about AI's potential and its pitfalls,
Speaker 1: I wanted to end the conversation with some concrete advice
Speaker 1: for those of you listening who are struggling to find
Speaker 1: a job, and you might be surprised by what they recommended.
Speaker 2: I'm going to tell you something that is going to
Speaker 2: upset anyone to know that's in the recruiting world, and
Speaker 2: is going to be helpful anyone who's looking for a job.
Speaker 2: But the best way to beat the system is to
Speaker 2: stay out of the system. Find a human, find a connection,
Speaker 2: find a relationship, and play that up.
Speaker 3: One of my kids had a real problem getting a
Speaker 3: response from a company he had applied to, and eventually
Speaker 3: I told him write them a letter, physical letter. I
Speaker 3: don't know whether that's why he heard from them or not,
Speaker 3: but I'd like to think it is because they don't
Speaker 3: want thousands of people pinging them about jobs. They don't
Speaker 3: have time to read them, but a letter. There aren't
Speaker 3: very many come in so might very well get to
Speaker 3: the person.
Speaker 1: There you have it, folks, what's old is new again.
Speaker 1: So maybe instead of spending money in the latest and
Speaker 1: greatest AI subscription, we should all go out and buy
Speaker 1: some Forever stamps before they go up and price again.
Speaker 1: For on the job, I'm Avery Thompson.