A.I. Recruiting Tightrope


Candidates delivered straight to the Hiring Manager sourced purely by Artificial Intelligence?

It's happening already, recruiters are being cut out of the process and algorithms are taking over. On today's exclusive Chad & Cheese speak with Untapt's Chief Data Scientist, Jon Krohn about what is already happening in recruitment and A.I.

This exclusive brought to you by the masters of AI matching technology, Sovren.

PODCAST TRANSCRIPTION sponsored by:

Disability Solutions is your sourcing and recruiting partner for people with disabilities.

Sovren: Sovren parser is the most accurate resume and job order intake technology in the industry. The more accurate your data, the better decisions you can make. Find out more about our suite of products today by visiting sovren.com. That's S-O-V-R-E-N.com. We provide technology that thinks, communicates and collaborates like a human. Sovren, software so human, you'll want to take it to dinner.

Jon Krohn: Different candidates are looking for different kinds of experiences. Some candidates would rather have the fully automated experience. They're used to using Uber and Seamless for getting their cars and food respectively. They want to be able to push buttons and see statistics and get things immediately and have results happening in a fully automated fashion.

Intro: Hide your kids, lock the doors, you're listening to HR's most dangerous podcast. Chad Sowash and Joel Cheesman are here to punch the recruiting industry right where it hurts. Complete with breaking news, brash opinion and loads of snark. Buckle up boys and girls, it's time for The Chad and Cheese Podcast.

Joel: Oh yeah. We got a show virgin on today Chad.

Chad: What?

Joel: Never heard the show, doesn't know us. We are going to have some fun with this one. All right. Dude, Jon Krohn is our guest today. You don't know Jon, you probably don't know his company untapt. But Jon is smarter than five of us, PhD from Oxford, bestselling author. I'll let him do a little bit of that. But first of all, Jon, welcome to the show.

Jon Krohn: Great to be on. That was a frightening introduction.

Joel: Yeah, I'm sure your PR person just said, "Hey, do this show. Okay." And you're like, "Okay, I'll do the show." Also you're Canadian. Again, we have a 10 sorry rule here on the show, so you have to say sorry at least 10 times.

Jon Krohn: I will do my best. I'm sorry that I haven't already.

Joel: Although I loved your pre-show comment that you've been in New York long enough that you don't say sorry for anything, so I appreciate that.

Jon Krohn: Yeah. When I first got here, immediately I arrived in the train station and I said sorry to someone and they took my lunch money, so I learned right away.

Chad: Usually they look at you and say, "What the fuck are you sorry about?"

Joel: Was it a rat though is what I'm curious about.

Jon Krohn: She was a rat dragging a pizza slice.

Joel: Yeah, exactly. Exactly.

Chad: That's standard.

Joel: I will say I enjoy the ... So we're going to talk about your book a little bit, but the name of the book is Deep Learning Illustrated. And when I see Illustrated I always think of Sports Illustrated. I have to ask, do you have a swimsuit issue of the book?

Jon Krohn: I've actually, I've just been posing for it. Yeah.

Joel: Well, we can't wait for that to come out. And when it does, let us know.

Jon Krohn: It's only me. Yeah, I don't think it's going to sell well.

Joel: All right Jon, in all seriousness not really. But give us the scoop on you. Give us the scoop maybe more importantly on your company and what you do for them. Because Chad and I have been around for a long time and didn't know untapt. We've never seen you at a show, we've never gotten a press release from you, but you have real money and real investors and real people behind you. Here's your time to shine, go.

Jon Krohn: Yeah, it's interesting. I guess we need to be doing more marketing. We do have great people. We do have great people backing us as well. And we've been around for a while, so we've been around since 2014, but we existed primarily as a recruitment marketplace for most of our years. So up until February, 2019, when we licensed out our recruitment marketplace, we then began focusing on becoming a B2B business where we are selling recruitment algorithms, HR algorithms, user interfaces to wrap around these algorithms, with the idea that we can automate a lot of mundane tasks that HR professionals, recruiters have to deal with today. And because we're both removing the monotony and we're taking advantage of high powered algorithms across large databases, we can help you do things you couldn't possibly have done if you try to do it manually.

Joel: Are you more of a white label solution or direct to consumer? Recruiters, employers come directly to you and use your product or do people that provide services to employers use your backbone to power stuff on their stuff?

Jon Krohn: Right. We have three different kinds of clients. We have big corporates, we have HR tech companies, and we have recruitment agencies, there are three types of client. And those three kinds of clients use us in different ways. The recruitment companies account for most of our clients. They have us, yes, create white label solutions where people come and use websites. And that could be candidates looking for jobs. It could be companies posting roles or it could be recruiters using a platform to use AI to scan databases of millions of their contacts to find the right people. And so in that case for the recruitment firms, it's a white labeled user interface powered by our algorithms.

Jon Krohn: And the big corporates, they also use us in that same kind of way, primarily for mining their databases. These are companies, blue chip companies that everyone has heard of that have millions of applications a year. And so they need tools like ours to be able to sift through all those applications with something much more clever than a keyword search. For example, a microchip company that everyone knows, they use us and they did an internal study. They found that they could identify 21 times as many of the highest quality applicants from their databases as opposed to the keyword search that they were using previously. And then that final group, the HR tech companies, we work with them primarily to build algorithms exclusively, so back-end algorithms that can then plug into their existing tools. We will work with them to create custom solutions that allow them to automate aspects of their technology and then their engineers will be responsible for plugging things in and having that work with our algorithm that we built for them.

Chad: Do you have a consumer facing model so that job seekers can come directly to you?

Jon Krohn: Not anymore, no.

Chad: Okay. What's the actual purpose of untapt? If you can kind of laser focus on where your discipline is, what's your mission?

Jon Krohn: Our mission is to automate as many things as we can. We come from having that recruitment platform and so we specialize primarily in recruitment and human resources today. What we do within recruitment and human resources is expanding all the time. The kind of canonical algorithm that people are interested in from us is, I have a job profile, I'd like to find the best candidates for that job profile, so there's kind of job to candidate matching. But we also do candidate to candidate matching where you can say, "Hey, I have 20 great people on my staff. I'd like to find people just like them." And so we have an algorithm that can take as many profiles as you want, it could be one, it could be two, it could be twenty, 200, whatever. And then we can average that into a particular kind of person. And then we can scan across their applicant database of hundreds of thousands of people or whatever and sort from top to bottom. Hey, here's someone who is 98% like that average of those 20 people.

Chad: Your CEO says that untapt has passed the Turing test. Can you explain that to our listeners? First and foremost, 1950s, Alan Turing, human versus computer. How do you guys know that you've actually passed the Turing test? Is there check boxes? What do you actually have to do to say, "We've passed the Turing test."

Jon Krohn: Well, we haven't passed Alan Turing's Turing test. Alan Turing's Turing test would have us having a conversational AI, a chatbot that would be able to, you wouldn't be able to distinguish from another human. So you'd be talking to two different people or two different screens. One of them has a human behind it and the other one has a robot behind it. And if you can't tell the difference, then the algorithm has passed the Turing test. In our case, what we did is we've pitted our algorithms, which are identifying the appropriate people for roles and having them compete, having that algorithm compete against professional recruiters. Then we put the results from the machine, from our algorithm, and the results from the professional recruiters in an envelope. And then we have other recruiters from that same firm.

Chad: In the results you're actually talking about candidates. So the results are actual candidates and you have candidates in two different envelopes, one fed from the computer, one fed from workers.

Jon Krohn: Yeah, exactly. And ranked from best to worst for a given role. And then we have a judge come in who is outside the room as the recruiters worked, and the judge or judges try to tell which envelope was sorted by our machine versus by the people that work with them. And it just passed that Turing test every single time.

Chad: Well, here's the quick ... I mean, here's the best question. How long did it take the machine to sort through how many different candidates versus how long did it take the humans to sort through those candidates to get to that list? Because getting to the list is one thing. It's the journey of getting there and being able to cut all that minutia bullshit. How long did it take to get there and how many candidates did they go through to actually get to the envelopes on the table?

Jon Krohn: Yeah, Chad, I'm so glad that you asked that question. It takes about five minutes for our human recruiters per resume to figure out. If we give them six, it'll take them about half an hour to confidently sort six resumes for a given job. Whereas our algorithm can do millions a second. I mean, there's just no comparison.

Joel: How long till you put all the recruiters out of business?

Jon Krohn: Well, that isn't what we would do. We actually we're quite dependent on recruiters staying in business. And so what we're seeing is a shift from recruiters just manually sifting or just constantly, every time they get a new requisition saying, "Okay, I've got a new requisition, time to mine LinkedIn for people who will raise their hand when I look for the word Python or whatever." We're seeing a shift from recruiters doing that to saying, "Oh, we've already accumulated a database of hundreds of thousands of millions of people and right now all we're doing is keyword searching on it. Is there a more clever way that we could be mining this huge pool of data?"

Chad: That we've paid for already.

Jon Krohn: Yeah, exactly. That you've already put the time in. And then we can apply a number of different algorithms. We can say, "Okay, first of all, here's a job description. Let's find the top people for this job description from your million people. But on top of that, let's work with the people who are highly engaged. Let's work with the people who are looking right now for jobs as opposed to people who are passive and focus on those people first." So we have

Joel: How do you know that from your database, just the freshness of it?

Jon Krohn: Yeah. It depends on the database, but we have clients who have platforms where candidates can be regularly interacting. They might be receiving emails from the platform. Are they opening those emails? Are they clicking in those emails? Are they going into the platform and changing things in the platform? Are they making applications to any rules in the platform? There are lots of different ways that they could be engaging. There's lots of ways that they could be suggesting that they are currently looking for opportunities.

Joel: How does AI solve the problem or can it to say, okay. I'm a business, I've been online taking online resumes for 20 years. Someone that applied in 2005 obviously has much more work experience now than they did in 2005, but so even though you can go back to that data point or that profile, it's pretty meaningless now, right? Or does AI have ways that it can, I guess, I don't know, forecast what that person is doing 10, 15 years from when they first applied. Talk about that.