Four companies entered the ring to duke it out for European supremacy at TAtech in Lisbon for The Chad & Cheese Podcast. Here’s contestant No. 2, Tengai Unbiased, a recruiting freakin' robot. Enjoy this Nexxt exclusive.
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Chad: Welcome to Death Match Europe. Part two of four. This Chad and Cheese Death Match episode features Elin, Chief Innovation Officer at TNG, and Tengai. Death Match took place at TA Tech, on May 9th in Lisbon, Portugal at 5pm with a room full of TA Tech practitioners. The bar was open, and Chad and Cheese snark was a flowin'. Enjoy this special edition Death Match from Europe after a quick word from our sponsor.
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Chad: All right Lisbon. I need a beer. Hey, Joe!
Announcer: Ladies and Gentleman, please. Would you bring your attention to me? For a feast for your eyes to see.
Chad: Hello everybody, hopefully everybody had drinks, I mean the bar is open. Hopefully you're there. Hopefully, you are on your second one, or third at this point. Whose ever seen the Chad and Cheese Death Match? Anyone? Anyone? All right, excellent, so this is...
Joel: Bad Ass, right?
Chad: Not first. Not first for some of you. What about Firing Squad? Anybody listen to Firing Squad? Alright, alright, so today...
Joel: What happened to the last Death Match winner?
Chad: They were acquired.
Joel: Big money. Big money, and potential winnings?
Chad: You have a mic? You have a mic right there?
Joel: Oh, there's a mic.
Chad: Yeah, so today we have four. Count 'em, four startups coming up to Death Match. They're going to have an opportunity to have a two minute pitch, no demos, no robots. Okay?
Chad: They're going to come up, they're going to pitch. Two minutes. Then with the balance of the 15 minutes that they have, we're going to belt the hell out of them with Q & A. Okay?
Chad: Big applause. All right, next on stage. You've seen it. You might have even touched it. We had a robot, or a couple of robots out here, so for you guys to peruse. Now we have their Chief Innovation Officer all the way from Sweden. I'm going to screw up her last name. So we're just going, like Madonna, just one name. Elin!
Chad: That's her name.
Chad: All right. All right, all right, all right
Joel: I'm a little scared.
Chad: Yeah, no she came out. She came out ready. That was a hell of a high five! So no. Don't start reaching. All right, so is everybody ready? Cause we're going to talk about Tengai.
Joel: If Elin's ready, and she looks ready.
Chad: And go.
Joel: Then, go.
Elin: Did you know that it only takes, like seven seconds for someone to make a first impression? Seven seconds. Now, I don't even want to know what you're thinking about me, right now. After just like 30, and with this crazy outfit, but let's face it. We are all biased.
Elin: Tengai is a diversity and inclusion software that is unbiased by design. With the human touch. Tengai is a social interview robot that will assist recruiters in hiring measures in the early stages of the recruitment process. Screening for soft skills, and potential. That will allow hiring managers to get more objective interview data so they can make better hiring decisions. With Tengai we eliminate gut feeling from the interview. Since the robot doesn't care about age, looks, gender, or ethnicity she can also interview more job seekers efficiently. The end result will deliver more diverse and efficient teams. With skills set to meet the challenges of today's, and tomorrow's workforce. And, but most importantly, it will free up time for recruiters and hiring managers to really engage with candidate at the end of the recruitment process where the human relation...
Joel: Thank you.
Elin: ...is needed the most.
Isabelle: 10 points for the outfit. (she's a fucking Viking)
Elin: Thank you.
Isabelle: So, presumably that's the softwares written by software developers. We know that tech is a very male dominate industry. So how can you ensure that something that's built by a pretty bias, heavy industry is going to be bias free?
Elin: Well. Good question. We know that's important to understand what kind of data that actually triggers bias. So it's important to use a very diverse workforce when they're programing the robot. So we use that. And, also it's important for us to understand the kind of data that triggers. And also, when we put that into an automated product we don't use any prior data, for example. And we also check the code constantly to understand how it's measured, and how the outcome is of the code.
Joel: So I think most of us interacted with the robot this morning. I have some questions about the scale of this. So it's an actual piece of hardware. So, how do you get over the, I guess the question of, why would I have a piece of hardware that I have to sort of scale through my organizations? Versus maybe a video, or something other that's digital that's easier to transport, or scale with an organization?
Elin: I think that you need to look at the efficiency of it. Because naturally it's a hardware, but also, we need to look at what it actually does. So, well you have all these kind of software that is naturally downloaded with a credit card, and all that kind of stuff. But this is the real stuff. We know that is why we will also use a human robot, or physical kind of robot. Because we know that it's important to interact with someone. You need to get facial expressions in order to feel safe, to feel secure. But the scale up is, well we have the production, and it's easy to scale up. The thing we are using on for the platform is a software. So that is easy to scale up, as well. So the scale up isn't really a problem.
Chad: So what specific measures are you currently taking to ensure that bias is out of the robot? Because, I mean we saw Amazon. Which is an algorithm, right? And there was bias. Obviously they had to scrap that code. It was an algorithm. Which, even though it is hardware there is the algorithm, or the software, inside the robot. So how do you ensure that that algorithm does not become bias, much like the Amazon algorithm did?
Elin: Yeah. Again, it's important to understand what kind of data that actually triggers bias. So we don't use any prior data into the product. As of age or gender, or that kind of data. So naturally it won't trigger from that. And also we don't use, like pictures, and that kind of stuff. And also it's important for us to understand how we can look at the actual answer instead of the data. So we use different kind of experts, in terms of you know looking at the validity of the product. So we listen to answers, we look at the data, and we sort of watch the code. And we do transcriptions of the code, as well.
Tanya: As with any kind of technology. Especially something that's new, upcoming. To a company, or to an organization, what is the cost of integrating something like Tengai? What type of value, do you, are you promoting in order to offset that cost? Do you have any statistics regarding, that, "yes this is costly". If it is. "It's costly. There's a lot of support. Tech support. But we have shown that by using this your process has improved by x percent". So do you have those statistics? And are you using that in order to be able to convince organizations to adapt this type of AI?
Elin: Yeah, first of all we are still early stage. So we haven't been sold the product, yet. So that's one key point. But we are launching the first product in a week. And that is an in-house product where we going to make, sort of, proof of concept to understand how we are going to use it in the future. But the business model is a licensed kind of model. And also, we understand, of course, that it's important to have a beneficial, like pricing model, of course. And yeah. The integrations isn't costly. But you know you licenses the product per month. And you do. Depending on what kind of volume you need. Depending on what kind of service level agreement you want. So it depends on that.
Tanya: If I was a recruiter I would be terrified, right now. Because obviously all these tools want to make your job easier, but they want to keep you in control of the human element of really judging someone when they come in. How they perform in an interview. Especially, in rolls likes sales, for example, where you need to have a certain appearance, certain soft skills, great body language, and what have you. Who is your market you are selling to? And how do you get around that fear of automation in the recruiting industry?
Elin: First of all, this is the tool for recruiters to make their jobs better. It's a tool for hiring managers to make them do better hiring positions. I think that the product is mostly suited for processes where you have a high volume of candidates because it screens for personality traits, and for soft skills. So you need, sort of, a high volume to understand the unbiased part of it. And in terms of interaction with robot, it's important to understand that it only asks questions that are combined to personality traits and soft skills. So in terms of being a sales person, which is basically the one, the people, that are most afraid of this product because they are not supposed to. They're not allowed to sell themselves as human beings, or as they're with their skill sets because the robot asks questions, such as, they are situational based. Connected to like problem solving, or service orientation. That kind of skills that we know are predicting future potential.
Elin: So people have asked, like sales personnel, they think that the robot is, you know sort of, you know it's not possible to sell myself because the robot treats everyone equal. It treats everyone fair. It gives everyone a fair shot at the job. And you can prove your skills set by doing that.
Joel: Much of the recruiting process happens outside of a person in a room interviewing, a hiring manager, or a recruiter. There are phone calls, there are chat bots. There is communication before that. Is there, are there any plans for Tengai to be part of the entire recruiting process? Will it make phone calls? Will it be a chat bot in the future? So when someone comes in for an actually face-to-face, that they are not surprised there is a robot on the other end of the table.
Elin: Good pitching. Yeah I think that one might be a good, like a product development. Which would keep you in the product development team. I think that, you know, it's important to understand also that the robot is, you know, developed from 15 years of doing unbiased recruitment. So we do like the job ads, for example. It's important for us to question the job description. So even though, the robot is only doing the interviews at the moment. I think that is a very good, new feature for the future. Engaging with the candidates at the early stages of the process. Saying like, "Hey, I'm Tengai. You should apply for this job. I'm very kind. I'm very warm. And this are the benefits of applying for this. And this are also the, you know, this is the company". Giving all the perks of applying for the job. So I think that's a good feature for the future.
Chad: So, Tengai currently is only fluent in the Swedish market.