Hilke Schellmann: Is Your New Boss a Robot?

What I have learned by like bringing AI into the talent acquisition hiring space, I learned like how bad our old processes are, like job interviews. Actually, really bad. Um, because you are uh it sort of filters out the people who are good about talking about doing the job. As opposed to doing the job. So we have this like confidence versus confidence problem. Like people who will like come off as like confident, we often think like well that person

speaks so confidently about the they must be really good. It turns out like that are more often than not men. Um and that doesn't mean actually they're competent. So we sometimes complain of No. Never. What so you know. No As always, not all men, but a lot. We do us. This message is a very good idea. Partnership with Apple Card. Imagine this: you're at a checkout counter, you're ready to pay when you realize you don't have your wallet.

You could drive all the way back home and you could get it, but you remember that you have your Apple card on your iPhone. So you can tap to pay with Apple Pay. Imagine that, no need to carry a wallet. But you know, one of the things I do like about having my card on my phone is we live in a world where you lose your card and then you don't know where it is. And then you're like, what do I do?

Well, if your phone is connected to your card and your card is connected to your phone, you know what's going on. The best thing about having the Apple card connected to your phone is you know what every transaction is. You know like sometimes you're like, what did I spend this month? The Apple card will show you. One month I had spent an obscene amount of money ordering videos online. Just videos.

They were just videos. What kind of videos? That's not the point. The point is, I knew that I didn't want to order those videos anymore because I'd spent too much money on it. It was videos on how to not spend money online. I felt like I'd been duped. Point is Apple showed me what I was spending my money on and I was able to change my spending habits and you can do it too. I earn up to 3% daily cash back on every purchase with my Apple. That's unlimited daily cash back.

shop. Apply for Apple Car and the wallet app on your iPhone. Subject to credit approval. Issued by Goldman Sachs Bank USA, Salt Lake City Branch. and more at applecar.com. All right, Eugene, let's play a little game. You know, make something fun. Two truths and a lie. Here we go. One, I've had to tell a world leader that their fly was undone. Two, when getting dressed, I don't do sock, sock, shoe, shoe. I do sock shoe sock shoe.

Three, I've been a Verizon customer for 11 years. What do you think? Very confused. First of all, why would a world leader owner fly? 'Cause those things just come uninvited. Secondly, lying to your friends is not cool. There's never been a game. No, Eugene, the f a fly is for like the zip is what and then it's it's it's not a lie, it's a game where I'm trying it's like I give you information.

Okay, I lied. All three are true, Eugene. And in case you were thinking, you know, Verizon isn't as expensive as you think. In fact, if you bring in your ATT or T-mobile bill, they'll give you a better deal. And the reason I've been with them for this long is just because I travel so much. I need a network that's reliable. That's right. A better deal on the best network, with the most ways to save on planned streaming and phone deals.

Take your ATT or T Mobile build to your local Verizon store today, get your better deal. And start saving for real. Based on root metrics, best overall mobile network performance, US second half twenty twenty five, all rights reserved. You must provide recent consumer mobile bill in the name of the person redeeming the deal. Additional terms, conditions, and restrictions apply.

So do you understand how two truths and d do you understand it now? I understand that you didn't have to lie first before telling me that Verizon is the best. No, I wasn't lying f Eugene, it's not a lie. I wouldn't lie to you. It it's a game. I'm sorry. I lied.

Are you based here? Where are you based? Uh yeah. Um based at NYU, Twenty Cooper Square. I live in Brooklyn. Okay. Oh, what part of Brooklyn? Green Point. Green Point. It's an old Poldish neighborhood. Why did your voice go down when you said Green Point? Well it's be very different. I've been in the same apartment for sixteen years. It's been beautiful sixteen years ago. It's still kinda beautiful, but the neighborhood is changing a lot. But it's isn't it becoming cooler and younger? Yeah.

Well, I like that it was like kind of Polish and you walk into a store and people talk to me in Polish and I don't really know Polish. The only thing I know is like one line that's like Nimien spot and that is really bad in Polish saying I don't understand Polish.

But I kinda like that. Wait, that's that's Polish for I don't understand Polish. Well, it's very bad Polish, I was told. But it was enough Polish that the Polish realtor was like, Whoa, I've never met a German who speaks Polish. I was like, Well, I just said that I don't speak Polish and it took me six hours from the train from Berlin to Wausau to learn this one phrase.

Um, because Polish apparently is very hard. Um so I kinda like that about Green Point. And that's like becoming exceedingly less, but the uptick is like the beauty of it now is like we have beautiful restaurants. Yeah. So that's pretty cool. Yeah. Um maybe I'm just getting old. I I've never understood why people learn the phrase, I can't speak your language in another language. 'Cause they want to be polite. And I think your language is a test.

On yourself to see how much you can learn. Okay, but now think about this too. Think about what this says to the other person. Yeah. You've said to them in their language, you can't speak their language. Yeah. To me, what it shows me is you just don't want to speak my language.

Because you've learned enough to say you can't speak it and then you won't learn the rest. No, I think that's a good thing really polite. You're like visiting them and like you wanna be nice to them. You've literally walked up to somebody. And you wal someone came up to you and they were like, I don't speak English.

And then you're like, well you did a great job there. And they're like, mm, that's enough for me. Think about it. You're like, nah, that's enough for me. That's good. Well, um Hilka, welcome to the podcast.

Uh well thank you for having me. Thank you so much for joining us. This is I like you know, sometimes and maybe maybe it's confirmation bias. Sometimes you'll see a thing in the world that confirms the feeling that you're having and the idea and and I and like a lot of us will be like, It's a sign, it's a sign. literally coming here into the into the studio today, I saw these posters that are all over New York. It's a little QR code and it says AI

Who are the winners? Who are the losers? And it's a QR code, and I don't know what's happening, and there's all these different ones everywhere. And then they say, Is your job next? is your job next. And it's all like ominous and it feels like it's promo for a movie, but it's not, I think. So what is on this QR code? Did you check? I'm not gonna scan a random QR code. This is how your phone gets hacked.

I'm not gonna scan the cure. I was just I was I just w looked and I was like, yes. I wish I did, yeah. I was like, we're talking to the perfect person today because you have dedicated more time in your life than most people. into answering this question. who are the winners and who are the losers. So like be before we we we delve into it, like if if if you were to explain to somebody

who you are and what your passion is in and around the topic of AI and it how it relates to work. How would you introduce yourself to them? Oh wow. Um I guess I feel like, you know, I'm an investigative journalist and I have you know, I used to investigate all kinds of things and now I just investigate AI and I'm trying to understand like

How does it work in society and maybe who are the winners and the losers? Uh, but also like, you know, I really think about like what it's changing the world of work and I saw it eight years ago starting and I was like, Oh, I don't know if people are aware of this and somebody needs to look into it and There was kind of nobody else there who was like looking into it. So I was like, might as well look into it. Um, I'm just driven by like sort of curiosity and I'm like, what is going on here?

So now it has a little bit involved. Like I investigate AI, um, not only AI and hiring and in the world of work, I also build AI tools. I think about like how Journalism will be impacted by AI and how we can maybe safe journalism or a factual based society when everything can be generated. Um so those are kind of things and questions that I think about. I love the idea of being an investigative journalist, doing everything and then focusing on one thing,'cause then it makes me go

What was it about this one thing that you thought supersedes everything else? Like what what were the other topics you were covering before this that you that you uh you know, like uh uh violence against women in in Pakistan. I went to Pakistan, I looked at like South Asia, I did all kinds of things. Um

And I don't know, I had like one lift ride in 2017 in the fall. I was in Washington, DC, trying to get from a conference that has nothing to do with AI, uh, to the train station. I got in the back of the car and asked the driver, How are you doing? And he said, I've I've had a weird day in the history of me taking lifts and no one has ever said that. And I was like, really? Well what happened? He's like, I had a job interview by a robot with a robot. And I was like, what?

jumping in me with a robot. Um he said, Yeah, I you know, he had applied for a baggage handler position at an airport and he got a c call from a robot that asked him three questions and he was really weirded out. This was in two thousand seventeen. So we are, you know, light years uh further down the road of AI now.

Um, but I was like, I have never heard of this. So I started looking into it and here we are. And then I went to a conference and I was like, wait a second, there are all these like AI vendors in HR and like it's being used everywhere and no one talks about it. And whoop, down the rabbit hole I went. Um, and somehow it never it doesn't let me go. I'm thinking about like the next four books on AI, the next research studies on A. I it just doesn't um I don't know how I

I don't know. I'm very I'm very bad at predicting the future. Uh, but I could tell that this is like a transformative technology that we need to pay attention to and not only how the technology works, but it's like societal implication. What does this mean if we use AI in higher What is it? What are the consequences of this? If we use it in journalism, how does our world change or maybe not change? And how does it improve the world or maybe not? And I was surprised that.

there isn't maybe a whole lot of improvement as we wish it would be, at least in hiring. Mm-hmm. So I think that was a little bit surprising, sadly. Um, that when I first saw like the first time I went to a conference and somebody was explaining how they do like

emotion scanning on their faces and like checking the intonation of your voices to find out if you're gonna be good at a job and like the words that you say and I was like, Wow, who knew that like facial expression in s and job interview could be predictive of your success at a job? Like

What what a way like a new way of science. And then, you know, we trust but verify as a journalist. So I trusted that information and then I went on to verify it and talked to a lot of experts who are like, what? emotion on faces like that doesn't exist to predict how good you are at a job and I was like I was like, oh, that's too bad. Um intonation of all our voices. We can't really tell what kind of emotions you have. Like we can sort of like

make a prediction, but that's not always really the case. Like, you know, it's kinda like when I'm in a job interview and I say I'm nervous and uh sorry, uh when I'm when I'm in a job interview and and I smile and people, you know, facial uh emotion scanning algorithm would say like oh yeah she's totally happy she's smiling and I'm like I'm not happy in a job interview. Who in the world has ever been happy in a job interview?

Uh so that's kinda like you know, it is a prediction, uh but we're using it to like sort of select people It's just like your intuition fr from what I hear, it's like your intuition as an investigative journalist was basically to say There's something deeper that's happening here. There's a world. Do you know what I mean? Yeah, totally. And we somebody has to look into it. And for some reason

It just sometimes happens to be me who's standing right there, so I have to like take it on. It's like, you know, when them When the uh chairwoman of the Equal Employment Opportunity Commission, when I was talking to her about AI and hiring, and she's like, Yeah, I do wonder. Now we have these like one way video interviews and and you know, the the companies use the recording, run them through a transcription service, like speech to text transcription like you have on your phone.

Uh and then the AI predicts upon that transcription. And she was like, I wonder how good the transcription software works for people with accents, people with speech disabilities. Yeah, totally. And you have like a federal agency. You should totally look into that and study that. And she's like, Oh yeah, I don't know. And I was like, Okay, there's no one here. So I started to study it with the help of a research team, a computer scientist, sociology professor. I don't do this work alone.

Um, but um yeah, so that's kind of the work that I do. You know, the more you speak, I realize this is how it sounds like whenever I speak to Trevor about technology. He knows so much about technology. I only know how to send tech. But you send them very well. Very well. Sometimes I send pictures as well. With dog text. And an emoji will get me started. You know, I've actually never heard you talk about AI now that I think about it. Never.

Because also I don't understand how much of it is in my life. And I don't also understand how much it scares people. So I'm even scared to ask people, what is it about AI that scares you? Because I don't interact with technology that much. So how would you explain to me what scares people and how much I've been using without even knowing I've been using it? Yeah. Um well we use it in everyday life. Do you have a spam filter on your email? Okay, I specifically said to you.

Uh well, you know, it's it's like sort of the the rise of the of of AI has been everywhere, right? And it's really like software, really, what it comes down to. It's just sort of like maybe software and steroids. It does things better than we used to, where we say, like, oh, if this, then do this. Like we have now self learning uh tools that can sort of do translations. Um from, you know, we could now be talking in German or French, and an AI could just translate that in our voices.

Uh and NAI can generate that. Um so we see it kind of everywhere, moving into everything. That's crazy. You like so wait, you're saying with the technology now, out of nowhere, we can just go from speaking English And then we just switched into another language. In real time. In real time. I don't know if it works in real time, but we can definitely do it. I can definitely do that. Yeah. Und dann du sprech Deutsch. Und dann Eugene sprechst Deutsch. Du auch? Yeah, I spread out Deutsch.

Okay, you do not have to emulate the AI. You know, you uh y your book your book really um I think shook me up in in in in the perfect ways because You've written extensively about about the world of AI and I and I what I wanted this conversation to do, because I I I try to talk to people like Eugene, funny enough.

who I realize don't have the handle or the or like the passion for tech that I have. You know, and and sometimes I think if you love tech too much, you're just focusing on like the tech side of it and technically and you're like, wow, the engineering. And then when I speak to a person who's not into tech, they just go like, wait, wait, wait, wait.

What does it do for me? What does it do against me? And how do I need to think of its role in my life? And y your book really broke it down because one of the first things I noticed uh about your writing is AI is fundamentally gonna change what the word job means.

Do you know what I mean? Like, like job has constantly had like evolutions over time. Like, people used to go, like, a job is this, and you know, like it meant using your hands, and people like that's not a job. And the first people on a computer will think they're like, That's not a job, and then now people go, That's not But fundamentally, from everything I've seen you write and obviously everything that's happening in the world

It seems like job itself is gonna change. What have you found in in your investigations on like how AI is changing? what jobs actually are or aren't, like in different fields, lawyers, doctors, et cetera. Yeah. I mean I think we already see some of it coming down, um uh uh You know, we see we we already see some of the consequences of like AI infiltrating our our daily lives. We see a lot of uh way less like sort of early career hiring.

'Cause I think a lot of times people who use AI a lot sort of uh describe it as like, Oh yeah, I have like a little intern with me who does like a lot of jobs for me, right? Like they can write code for me. They can do uh you know, you can generate a research report of stuff that I need to know. Like I can generate

emails, newsletters, like stuff that I have to uh write that that we maybe were gonna give into calendar book my flights. Yes. Yeah. Remind me of stuff. Yeah, totally. Totally. It can it can do a lot of that. Um, you know, we're still thinking about like still are looking into like a gentic AI. Can it really book the best flight for you that you want? You know, we still are working on that, but can definitely

um, help you like generate research, doing math problems, all kinds of things. Um so I think we we see a lot of companies already moving towards like, oh, having fewer headcounts and sort of like I worry a lot about like what is the how's this pipeline gonna break of people um, doing like um early entry jobs, um, how they're gonna get the expertise and the wherewithal to like move up if we sort of take out the the first layer of jobs. Um like upskill people.

And um But how do we how do we that that seems to be the conundrum, right? Is law firms Most of the people who start out in a law firm start out they've got their law degree, they go and work at a law firm and it sounds like your job is just to like go through the paperwork and do the research and write up briefs and do this th but you're working for someone, but in that process you're learning.

And they're teaching you what they're looking for and they're trying to but y if we cut off that level then where does the expertise come from? Yeah. Because we say upskill, but then who's doing the up of the skill? Yeah, yeah. I mean, I think it's like sort of a an um, you know, what I sometimes fundamentally think of, and, you know, we don't have all the answers yet to some of these questions, if I may say that.

is like sort of like what what stays as a human, uh, in the age of AI, right? If like uh AI can do sort of what we think as like very human uh things, like if AI can write better than I do. How can I express myself? Like, and what does it mean for humans in a world of AI? Like, what do we bring to the table now that AI can do so many things for us? Don't go anywhere. What now after this?

All right, Eugene, let's play a little game. You know, make something fun. Two truths and a lie. Mm-hmm. Here we go. One, I've had to tell a world leader that their fly was undone. Two, when getting dressed, I don't do sock sock shoe shoe. I do sock shoe sock shoe. Three, I've been a Verizon customer for eleven years. What do you think? Very confused. First of all, why would a world leader owner fly?

'Cause those things just come uninvited. Secondly, lying to your friends is not cool. There's never been a game. No, Eugene, the f a fly is for like the zip is what and then it's it's it's not a lie, it's a game where I'm try it's like I give you information Okay, I lied. All three are true, Eugene. And in case you were thinking, you know, Verizon isn't as expensive as you think. In fact, if you bring in your ATT or T mobile bill, they'll give you a better deal.

And the reason I've been with them for this long is just because I travel so much. I need a network that's reliable. That's right. A better deal on the best network with the most ways to save on planned streaming and phone deals. Take your ATT or T Mobile build to your local Verizon store today, get your better deal. And start saving for real.

Based on root metrics, best overall mobile network performance, US second half twenty twenty five. All rights reserved. You must provide recent consumer mobile bill in the name of the person redeeming the deal. Additional terms, conditions, and restrictions apply.

So do you understand how two truths and d do you understand it now? I understand that you didn't have to lie first before telling me that Verizon is the best. No, I wasn't lying for Eugene, it's not a lie. I wouldn't lie to you. It's it's a game. Okay, I'm sorry. I lied. Ah Eczema is unpredictable, but you can flare less. A once monthly treatment for moderate to After an initial four month or longer dosing phase, Four and ten people take it.

Emglis Libri Kisumap LBKZ, a two hundred fifty milligram per two milliliter injection is a prescription medicine used by Or forty kilograms with moderate to severe. Therapies used on the skin or topicals, or who cannot use topical therapies. Epglis can be used with or without topical corticos. Don't use if you're allergic to Epglis.

occur that can be severe. Eye problems can occur. Tell your doctor if you have new or worsening eye problems. You should not receive a live vaccine when treated with EBGLIS. Before starting EBGLIS, tell your doctor if you have a parasitic infection. Ask your doctor about Lily.com or call one eight hundred LilyRX or one eight hundred five four four four four four five five nine seven nine.

Hi, I'm Kaitlin Coleman, winner of Target's HBCU Design Challenge. This challenge moved me closer to my dream of becoming a fashion designer through mentorship and support. You can find my design along with creations from other Black founders in Target's Black History Month collection.

In in the job space actually, I I would love to know like you've done a lot of investigating and I wanna get into some of the stories because I I think people will be fascinated by how humans have been affected by AI already. Is there is there is there like a concrete number on how much hiring is actually done by AI now and how much is human? Because a lot of people out there, if you told them, Oh, hey, your job application, your C V, your resume, whatever you type up,

It's not even seen by a human in some companies. Yeah, nothing. Yeah, sorry. Um so we think of it. How do you think you got here? You think if I knew you were coming you'd be here? If I looked at your message, I'm not sure. You now have to say it was like shitty AI or something. This freaks me out ev at every at every turn. Wait, wait. So someone applies for a job.

So you like upload your resume or you don't even have it uploaded. Uh like you already have it on LinkedIn and you just hit hit the one click. Yeah. So To the company I'd like to work for. Yeah. So like all of these big platforms, they all use some form of AI. That I can tell you. We don't have like a central register where companies have to register and say like we use this AI tool or not. We just know this from surveys and

sometimes me calling companies. Um so I know that they use AI. So you have to think about like the beginning of the hiring process, you often have thousands of people applying for a job, right? We call this like sort of a big funnel. And uh some companies, you know, this is um

uh a couple years old when I talked to Google, they get over three million applications. IBM gets five million, over five million applications a year. So it's a lot of resumes that come into this funnel. So what we now see is like um a lot of companies and usually large companies, a lot of Fortune 500s. Uh use AI to reject the U.S.

people to sort of call the herd of all these applicants. And like so we see in the early stages, uh, rejection, rejection and like a few people uh going on the yes pa for AI and then you know, doing like one way video interviews and now we have video avatars interviewing people. Just to just break down what is a one way video interview? Because I think a lot of people I didn't know what that was until I until I read the I hear you. Uh I've done

No, but I didn't know. Me. Yeah. Yeah. So like a one way video or audio interview, like uh, you know, there's now a traditional way to do this, which is like six or seven years old, uh where you don't have anybody else. uh on like, you know, you kind of log in, you get a link, um, do this uh video interview if you want the job in the in the next 48 hours. So you click the link. And then instead of a human on the other side, it's on a Zoom call.

Um, you just like get maybe a video of somebody saying, Hey, welcome to Company X. We're so delighted you are here. We have a couple of tests for you. And then you get a question like, What are your strength and weaknesses? Why do you want this job? And then you tape yourself basically. You get like a couple of minutes of prepare. And then you tape yourself like saying, like, my strength and my weakness is this. Uh, and then I think all of the uh applicants I've spoken to think that like

A human watches all these f videos, bless their hearts if they do. And some companies actually do have humans watch all of these. But some companies also use AI uh to uh rank people and uh uh do that. So we see that more and more, and we see this often like entry-level jobs, we see this in like uh retail companies, fast food like um it's called uh

High turnover, high no, high volume, high I don't remember. Um so it's it's a jobs jobs that generally have people where people are coming in quickly and leaving quickly. Like they're not gonna be there. It's not a career job.

So people are going to Sometimes it's a courier job. Oh, but it's just like high turnover. Yeah, but it's a it's a high turnover or you have like lots of candidates that you have to go through. Um so for example, like Goldman Sachs said um if a few years ago for their summer internship they had like over a hundred thousand applications. Um, so they they have to like go through these applications and like narrow down the pool. So you use like resume screening AI.

Uh you use like uh video interviews, you can use games. Uh we see like uh personality. I these games are supposed to find you personality. um while you're clicking on balloons, pumping up balloons, they find your personality. All kinds of ways to assess you without maybe putting in a whole lot of work because humans are expensive to do this work and also

Sorry to say this, but in a lot of humans they do suck at hiring because we have human bias. But that's I suck at hiring. But this is the conundrum though. So so so this is this is the thing that's like weird now just for this part of it is My reflic my reflex when I hear something like that is to go, oh no, this is this is not good. How can you have AIs screening people's interviews? And but then on the other hand, I go, if you have a hundred thousand people applying to a job. Let's be honest.

I don't think there's any human who is going to get through those hundred thousand applications. I don't think there's any humans. And I wouldn't be shocked if there were like a bunch of humans who were skipping through this before because they were just like it's like auditions in a way. At some point the person's tired. Yeah. You want to get them when they're in the mood. Yeah, not when I wonder I wonder is there a world where like does the AI make it better then?

You know, I wish I could tell you that. Um So we don't know. We don't know. I've asked many, many companies um to let me come in as a researcher and like sort of uh look at like here's your traditional way of hiring, here's uh your AI hiring and have this like run at both times and then sort of double check like, you know, the people that the AI said that would be high performers, did they actually turn out to be high performers? And I have not seen a company

do this or wanna share this with me or with anyone. I think it's because I don't know. There's like a lot of turnover in HR. Like these processes don't work that that well. And I think what we already know, so what we know from a survey of uh C suite leaders, like sort of leadership in companies. um of over two thousands in Germany, the UK, and the US, uh, when they asked them if your company uses AI tools.

Um, do they reject quali qualified do they reject qualified candidates? And almost ninety percent um of the leadership said yes. So they know that their tools reject qualified candidates. they still use it because I guess the efficiency from uh using AI versus humans it's just much, much more greater. Um, but it's not that we know that one process is better than the other. I mean we do know that like Uh humans are very biased in hiring and even the best

anti bias training, it's not gonna get out of it. Um and you know, you we all know the shortcuts, right? If you see somebody on a resume that they went to Harvard, you're like, oh they must be smart. No. They're probably like you This episode, Eugene Cosa learns about the world. She's like, Wait, what are you telling me? But but but you go you know so this this is this is this is where I I feel like we stumble on the on the first conundrum.

Generally, generally, machines like predictability. Yes. Right? Algorithms like predictability. That's what an algorithm is fundamentally sort of trying to do. It's fine. It's fine like patterns and you know, yeah. And a pattern is a predictability. Yeah. Right? The conundrum or the paradox of being human is that

The biggest breakthroughs that have come from humanity have often come from the pattern breakers. The person who didn't think correctly, the person who didn't fit the algorithm, the person. So I Yeah, the outlier. So I wonder I wonder if companies in moving all of their resources towards efficiency and pattern recognition

might go the opposite direction of innovation.'Cause it's like it's almost like the misfits and the mistakes are sometimes the ones who give you the biggest blips. Do you know do you know what I'm saying? Yeah. Yeah, yeah. Yeah, yeah, totally. I feel like the solution has caused a problem.

Well, we were speaking about how many people had uh applied to Goldman Sachs and I think if it wasn't for technology, would you still get that many applications? That's interesting. Would a hundred thousand people from all over the world show up at the address to put in their resumes. So I think technology also allowed easy access. Because I also think there's people who know they don't qualify but would do it anyway. So why would you put a human through all of this? But also I think it's um

It's a a box ticking exercise for some companies as well. I think some companies don't want to hire anybody, but they'll just put out a thing that says we want to hire somebody. Yeah. Then they'll end up doing the internal process anyway because If you're gonna trust people with people's monies and files and information, you'd want someone that you know.

So I think companies know exactly what's going on, but they're just sending out hope. And I think once you advertise a job, it's a great way to advertise your company as well. Yeah. Yeah. I mean sort of like people online, you know, they they often joke because obviously some people

obviously are very aware that companies use AI and now a lot of uh Uh you know, I think I I think it felt very uh like passive and and and sad for a lot of applicants until sort of LLMs and ChatGPT and other AI came around. much easier for for me as an applicant to generate a resume. There's actually now So now it's AI warfare. Um yes, it is AI versus AI. I'm gonna use the I'm gonna use the AI to apply for the job. They're gonna use the AI to grade me. I'm gonna use the AI to pass the grades.

And they're gonna I mean try to use AI to like outsmart the AI. There's actually AI programs that now apply for you. Um so you don't even have to do anything. Um so there's all kinds of stuff. But like the question is like, well what are we then doing here? Like Yeah, like what are we? Yeah. What are we doing? That is that is a great question. That becomes the question. What are we doing? Because if The AI is hiring the people who are using the AI to get the job that the AI has hired the people

Then we that's what I mean, is like we have to ask the fundamental question, wait, what was the point of this process in the first place? Because multiple studies have shown Humans are terrible at predicting the future, especially when it comes to hiring. Right. A lot of the time when you're hired, you're hired because the person sitting across from you

saw something in you that they considered correct for the company. But a lot of the time it's just wrong. You know what I mean? It's just it's wrong. And then people don't do well and they go like, Well, that that didn't work. But it but the the prediction is wrong. you know what i'm saying yeah and so now

I almost feel like we we forgot what the whole point of an interview was. Like I I'm not a historian, but if I was to bet, I would think an interview was just to be like, Let me see what your vibe is. It was a vibe check. It was a vibe check. Yeah. But it turns out like vibe checks not so great actually. Like because you Like predicting who's a good employee. Yeah, but but also like a vibe check is like finding people who like

Like are often like have the same background as you. They speak like you, they vibe with you. Exactly. So you find the same people again. Um, but you know, we kinda know that like diversity is good for companies. Um, also like I mean, I think that's why we have uh, you know, fewer women, people of color and leadership uh positions because we have underestimated them.

as humans and hiring for decades and and and promotion decisions. So we have like uh sort of uh a lack of diversity already because of human bias and sort of the vibe. You know, you know you know, when you can come to a job interview, you want nothing more but like somebody, you know, like the HR manager or the hiring manager to like you. And then you start talking about like, well, what school did you two or like what did you do?

Oh boy, so you like this uh, you know, uh sports team, yada yada yadda. And that chit chat feels like very good for humans to make a human connection. But it's actually really bad. Uh,'cause that would brings the bias in because as and now as a hiring manager, I'm like, oh man, you went to the same school as me. It's so cool. I see you in a completely different light.

than other people. And I'm supposed to look at like what are the capabilities and like uh your skills that you need for the job. Not if we went to the same school, but we as humans do that. And that's where like a lot of the bias uh comes in. The unfortunate thing is you might think It's like A pattern machine that just finds patterns, right? And it will just look at your like capabilities, your skills and find the most skilled person.

But what we've seen in some of the AI tools when I talk to uh lawyers and and others who get access to these tools when like an AI provider, you know, they built the tool, an AI vendor, and a company may use their tool. Sometimes they bring in lawyers and do their due diligence. How does this tool work? And uh what they found out is um when the lawyers looked at it that uh the tool used um some of these tools use kind of problematic keywords. So for example

Uh there's the Amazon story that you wrote about. Um woman or women on your resume you got downgraded'cause you know the the the to the tool had learned over time, you know, you give it um uh uh resumes of people who currently work here or or who maybe made it to the last round of hiring, sort of labeling that them as these are the successful people.

Well, if you work in a tech company and you probably have a gender disparity already uh built in uh from maybe previous bias, um you kind of replicate that, right? If the people who are in the role, if use their resumes, the the she machine does what it does best. It looks for patterns and it finds out, Wow, women are less successful here. So we should downgrade them in the hiring process. So Yeah, there there were some applications in the story where Amazon was hiring people.

And their system basically went on its own, doing its job as it had been told, and it went, Oh, I've noticed. Women's Soccer team, women's, baseball, women's anything. does not match with the people who are currently at the top of Amazon. They don't have that word on the resume space. Exactly. So this person is less likely to be like that person. So we're gonna downgrade that. But this had nothing to do with your actual qualification.

Wait, did AI do that or did someone who put the input to the AI do that? No AI. Yeah, you have to think about like, yeah now, sort of uh present day AI what we do is like we give uh uh the AI just the data we have and let have it like we call it unsupervised learning, have it like figure out uh what do these people have in common and who should we hire so yes so it looks at like patterns in the the the resume lake um that you give it and I guess it scans all of the words.

And then then it does what it does best. It does um um a pattern um analysis and finds out, you know, and one other example was like If you had the word Thomas on your resume, you also got more points. If you had the word what? Thomas. Thomas? Thomas. Like the name Thomas. Um or like in an another case it was like words like Syria and Canada. What those got you up or down? That got you up actually.

Yes. Wait, wait, wait. If you name his Thomas on top of that. But now you have by intended. Yeah, I'll do it. No, but now so here's my question though. Does that mean that people could are there tricks that people could use now? So if I was writing a resume today Could I just write somewhere randomly uh Syria can't fashions reading about Syria? Uh Canada.

Maple theorem. Thomas Thomas. You know what it is? Thomas, Thomas, Thomas, Thomas, Thomas, and white. So I think the the the problem is that like most tools are like individually calibrated to each company. Well, Amazon had that women's problem. Um but they say they changed that. They also say that their uh machine learning algorithm was never used solely to make hiring decisions. Um But no one would say that it was.

Like I mean which company would be the I don't think I've seen a single story where a company has come out and said, Yeah man, we were just using a computer to choose who was coming here. All of them go like no no this was not. the only thing. This was merely a pilot program that determined You know, the more you guys talk, the more I realize Are we are we under you are a journalist, you know this, are we underplaying the role that biases have played in our lives?

People choosing whatever it is that represents a certain a group of people or a company even based on what they think the taste of the

of the population or demographic is. Do you understand what I'm saying? Yeah, yeah, yeah. Um so so you think in generally or in the hiring process? In the hiring process. Because if you're gonna work for a company and the person sits there goes, I think you'd be great here because of what what what what Now we are going because I think bias is always, and I could be wrong, always comes in when we speak of race.

Gender or religion. Once you've ticked those three boxes, we're like, yeah, but how many places have we gone to where there's that mix because of Someone's biases who decided maybe people who are six foot with muscles should be in construction and because they look like this, they sound like this, they talk like this, actually they'll be great for this job. So how many how how many of us are beneficiaries of bias? I think I think a lot of us are beneficiaries and a lot of us also um have uh

been sort of the victims of bias and probably unbeknownst, because, you know, you go in for for a job in a viewer, you you you you send in your resume and most likely is you get rejected, right? Because there's only so many jobs at the at the uh that are being Given out. Um, so the question is like, were you rejected? And I think most of his humans think, oh, well, I was rejected because I wasn't the most qualified candidate.

Well it might have been that you've been rejected because your name is Thomas, or in one actual instance there was uh the word African American that was used um to weigh resumes. In another instance, there was if you had the word baseball in your resume, you got more points. If you had the word softball on your resume. you got fewer points. So um that's probably gender discrimination. I would give you I would give you zero points for both.

In my company, I would be fair. You say baseball, you say softball, I would detract points. Trevor Burrus Do you see how it circled back? How those are the things that you're talking about. But now you know you know one I in a in a way, I know this this is gonna sound like a little crazy but like I can sort of understand these ones and when I'd read the examples in your work, I would go, This sort of makes sense. I can see why they've made a mistake here and they can rectify the

But there are some examples that you've given that that blow my mind. For instance, there's one there's one story that you go into of a guy, I think by the name of Mike, and he's he's like working for Bloomberg or he's like work trying to get a job at Bloomberg or something. Mm-hmm. And please help me understand this'cause from what I understood, I'll say it and then you let me know if I'm right or if I He had to play a game. Like candy crush type stuff of popping balloons.

And then he got fired because of how he popped the balloons? He didn't get fired, uh, but he did apply to a job. Okay. Um He was based in uh Barcelona and and and play and uh he was based in Barcelona and applied to a job in in London. Um and he got a link immediately after applying saying like, hey, go to this link. Um and you know, I I sort of feel like

We as uh job applicants, we sort of forced consumers off this tech, right? Because if you want the job and you get an email with the link saying like, Hey, you have forty eight hours, click on this link, yeah, play this game, what are you gonna do? You're gonna do it. Even though you're like and he was like while he was doing it, he was like This is where your wise asked me. Do you want to play a game? Why do I have to do this? Like it sounds great, and I think a lot of applicants

technically like it better than answering hundred questions about like are you the life of the party? Like I'd rather pump it balloons. Um but when you realize, wait, is this the only criterion I'm gonna be judged on? How well I like pump balloons or like Uh in in in in in one of the games I had to hit the space bar as fast as possible. And while I was doing that, you get like fifteen seconds or so to do that. And I was like, what does it have to do with the

Like in what jobs do you have to hit the space bar as fast as possible? Maybe it's like a company where like there's like big gaps between people's names. Maybe there's like maybe you're working at a company where it's like suspenseful pause incorporates it. Maybe it's like I mean, I wanna know what this job is now where somebody out there is just like uh Yeah Maybe it's a company Maybe it's a company that had to cut costs because all the enters

the enters on the keyboards were broken and now they have to hire people who can use space to get to the next line because you can't just press return. You can't just press come on, come on. And then that boss was like, you know, we need we need people can press the space bar. Get me the fastest space bar bosses in the world. We found them. We found them. But you know, I mean what's interesting, like that actually that sweet suite of games was was used by like multinational companies.

Like we're talking like legitimate, not some random company. You're saying this is used by like big name companies. How fast can you press a spacebar and that's the thing? They say they're not actually like looking at your capabilities of hitting the space bar. It's like finding out how much like uh you know how risk averse you are, like what your personality is underneath this.

Um like are you somebody who likes challenges or not? Um I guess the space bars or something. Any order that you're given. I'm sure even the the time between you deciding are you gonna press the space button or not actually maybe counts. You know that's d did you really think about this instruction?

I d I don't know if that counts, but I did talk to industrial psych um uh industrial organizational psychologists uh who said, Yeah, we looked at all of those things and actually the people that take longer um until they start playing, they're actually less Successful, but he said we are not using that uh criteria. Touch my finger. You called it. You did call it. Um

But um so we don't know exactly but you know, all of these like every space bar hit and everything that I do obviously uh gets recorded somehow and can be used. But the question is like You know, on a good day, our personality is su is such a low predictive measure to measure how good we are gonna be in a job because it also turns out like I can overcome things in my personality, right? Like I don't know if any one of you have like I try to, you know, I used to be like

Really shy. I didn't like to talk to strangers. Um, I know it's part of my job. Um I like calling people on the phone and chatting with them, but like going to like like a party, like a reception with actual people, I don't know, and like Going up to them. It's like, uh I used to hate it. And then I was like, it's part of my job. And I made it I made it a game.

Challenge myself. So it's like I'm gonna I make bank games for myself. You just walked into parties with a keyboard and you're like, How fast can you hit this space bar? You win. Nice to meet you. Nice to meet you. I'm Hilka. We can be friends. This is this is my research. That would have been much more interesting. But what were the games? No, no, the the game was that I have to approach strangers and like saying, Yes, yes. Like, what was your final conversation?

My reward was just like, well, getting to know people and like learning I like this. So this was how you overcame it for yourself. You went, I'm afraid of speaking to people, so I'm gonna make it a game where I just walk up to a stranger, speak what happened when I tell my journalism student. What happened when it didn't go well? Uh well, I'm still here.

So I was afraid I was gonna get decapitated, right? People are nice now like Um, but you know, I'm still here and you know, sometimes people were just like, eh and like Just left me standing there and I was. Yes, but you see, this is AI again, um having let's say if this was a program, you would score higher because you're a woman. It's easier for women to do that than a man to do that.

Oh, that's interesting. If I walk to into a a random room, then there's a bunch of women there and I'm like, Hey guys Aren't you playing a game where I'm trying to do social? Stranger danger cycle. But for a w for for a woman it's much easier. So the bias is kick kicking again. If I go to a mistw Midwest town as a black man from Africa. And I walk in there and there's truckers and I go, howdy, folks. No one's gonna say hi to me. That was a good howdy though. You like that? I'm in.

If my eyes were closed when you walked in... Close your eyes now. Howdy folks. Hey, who's that over there? That was not bad. I'm I'm in. Darn. Once I look up, things might change. So you see how biases is informing how the uh what what the outcome ends up being?

'Cause it's not a good one. But it wasn't a biased challenge. It was just like a personality like overcome challenge, right? Because we all have like certain things that we like to do and we don't like to do. You were not biased. They were. On the other on the receiving side of it, they were like, here's a woman, she's smart, she's nice, she's saying hi, let's y let's threaten it. Yeah, that's true. Exactly. So the bias is kicked in. So the same

applies when an HR manager is sitting across someone who they look at and go, I wouldn't want to be stuck with you in an elevator on the fourteenth floor. But then but then that's six at night. Yeah, but then that raises the question then. Is there ever going to be a world without bias? And is that what we should be looking for? Or I mean look, we can all wish, but we know that that's that's never gonna happen. Like we hu we humans are biases machines.

Yeah, but but now that the but now that the machines are doing the job. Could it be possible and I know I'm not saying it will, but I'm saying could it be possible that the AI'cause here's here's what I think about in in what you're talking in what you're saying. We're living in a world where We know that biases exist. We know.

Right. So whether it's in courts, whether it's in law enforcement, whether it's in jobs, whether it's in schools, doesn't matter. We know that bias social settings bias exists. Right? Now AI's gotten involved. And we see the AI mirroring many of our biases. Yeah. But the difference is with AI, we can actually see it. We couldn't see it before and we couldn't like prove it.

We had to conduct like weird studies. We had to before you couldn't say this company didn't hire anyone because they didn't say baseball or because they had women or because they said black. But now you can't you can actually look at the data and go, Oh damn. And I I sometimes wonder if It'll be easier. And again, this could be the optimistic side of me, but I I sometimes wonder if it could be easier for us to address

bias in society because we actually have concrete data now that shows it and we get to blame it. We don't have to blame each other. We'd be like, oh my God, AI the racist AI was to you. I'm sorry, my friend. AI is the Trojan horse. You're right. Yeah, yeah. I mean I I wish companies would would would actually look at these

uh tools more closely. I think the the the the general notion though is they buy it from a vendor, the vendor sort of like you know sort of uh uh services the algorithm over time and makes sure they still run and there there's

less bias. Like the check if there's like gender and like very basic uh racial bias in there. But they never look at like, you know, does it let people with disabilities through or something like that, right? Like um and it also we don't see a whole lot of companies actually checking How are the decisions being made? Um, and I think that's sort of where the problem lies. Like if we actually somebody would look at the thousands of keywords, resume parsers used to predict.

um if you're gonna be good at the job, they would find those keywords that are learned from lawyers and other places. And, you know, those are keywords we shouldn't be using. We should be looking at like your uh skills and your capabilities and not if you are on the baseball team or not. Like you know, and I came to this as a human. I remember like for the first time talking to a lawyer about this and I was like, well maybe the AI found something that humans couldn't that like

in this case it was playing lacrosse in high school that was like a predictor of success. And I was like, maybe it found out for this like whatever insurance job or sales job. It was really good to play, you know, to play lacrosse in high school. It found this like hidden gem that we humans couldn't and Uh the lawyer started laughing and he was like, God, you think like a human? I was like, Really? What? He's like, It's a pattern machine. It does a statistical analysis. For whatever reason, like

uh playing lacrosse in high school, a bunch of people who were in the job have that criteria. Yeah. It doesn't mean that like lacrosse has anything to do with your success. And in fact he's like Well, if it's like playing team sports, what's with all the other team sports? Like why weren't they included? Why do you get uh more points for baseball and fewer points for softball? But is essentially

I think as a non American, it's a same game, just a bigger field. Hilk is on my team, minus points for both. Like how you saw pickleball. Oh beach ball. We call it beach ball. Don't don't bring pickleball into this, please. Please. Let's not bring Trevor doesn't want to talk about pickleball. Don't press anything. We've got more. What now? After this. Eczema is unpredictable, but you can flare less with eczema.

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Hi, I'm Kaitlin Coleman, winner of Target's HBCU Design Challenge. This challenge moved me closer to my dream of becoming a fashion designer through mentorship and support. You can find my design along with creations from other Black founders in Target's Black History Month collection. You know what I realized speaking to you guys about this?'Cause I I wanted to know as little as possible about the topic so I can get enlightened in real time. Is companies how's that going?

Very well.'Cause I I'm I'm dis'cause I've worked in retail before in South Africa and uh and I've realized that HR has always been the enforcer and the goon. of the corporation. Cause when you come in, they're the first people to ask you, what do you like? But basically they're trying to see, do you want to fit in here and be here? And where do you come from first? Then when you get let go, you do what they call an exit interview.

Yeah. And that will help them not hire a person like me ever again. So I use public transport. I went to a township school. So they knew that all of those factors and my age as well and how long I stuck around in that job. So they know the propensity of me sticking around longer or doing something wrong or right according to them. is based on how long I stayed and where I come from and what changes I've made in my life.

since I had started working there. So they could predict if someone earns this much for this long at this age from this background, the money will start becoming too little for them to be here. So AI now is doing that at a rapid rate. Instead of saying we don't want women, it will cut out words like soccer and blah and blah and blah and blah and then the people that say those words maybe they get hired because likelihood is they are men.

How many kids do you have? How far from the job you live and w how what are you willing to do for this job? I was gonna say like the you know, like um when you think about it, like how these kinds of statistics and and and prediction works, it's it the i it precedes AI by a long time, right? Like we know statistically that if you have a longer commute to your job side, you are much more likely to quit.

Statistically. But is that fair? And you know, we've seen companies um trying to use this, like zip codes and stuff, to then say like, Okay, well we only hire the people that are right, you know, live in the zip code riding around our store location because They are less likely to quit.

But like does it really have that that that's a criteria that has nothing to do with the job. It doesn't say anything about your capabilities and if you're gonna be good at the job. Yeah. And you know, and also like well First of all, like there are people who do the two hour commute each way and they do a fabulous job. So you're cutting out all those people, and it's not their fault.

Um and then on the other hand, y you also have to look like we live in very um segregated communities in the United States. There's historical redlining. So if you like start taking out zip codes, you might actually like take out huge swath of like um African American population or Asi Asian American population. And I think a real problem and we sort of see this kind of um

Uh, statistical bias get replicated again and again. But now we have this like layer of objectivity, and we don't interrogate the tools. Again to actually deniability to enforce me. How did you know that? I think it's plausible deniability um of the companies that use it and buy from the vendor because then they can't be taken you know, it'd be very hard to have a court case where you say, like, well, you knew that you too was biasing women

and there's like two million people that apply to this, two million women that apply to this company and you use the bias algorithm on them. So suddenly you have you like pop potentially two million claims. Um, that's why we see like sort of what I think is sort of a cloak of silence around this because companies also obviously don't wanna come out. Um, you know, I've had so many people who work in HR tell me like after the book came out, you know, oh yeah, we use that tool that you talk about.

And we, you know, stopped using it and I'm like, Oh, really? I was like, Well, that's good. I'm glad you did. They're like, Yeah, we sort of realized we had the same questions, we found the same things that you found and uh we just didn't think it was fair and I was like, Okay, can you can you talk about this? They're like, Oh, absolutely not.

Um, but we need to learn. Like we'll never get better. We never we we can't put pressure on the vendors to build better tools if we don't know how the tools work. Um and and if there's any problems in the tool. I just look Fraction of these tools. Like I tested some of them myself. I worked with like scientists to test them. I looked at like, you know, I spoke with like whistleblowers and like uh lawyers who like work in the space, but I have just a sliver of the whole

uh sort of world out there. Like we need to do a whole lot more, but I don't think it's in uh the company's interest. They want something that uh, you know, like sort of saves them money in HR. It's always a cost center. HR never generates money or talent acquisition, however you want to call it. And uh so in in a way they wanna save more money, have less

uh labor involved and they don't wanna like hire people who are now still like picking apart the algorithms then you know then it might not work and w what are they gonna do then? They just spend so much money in it. So when when you so when you look at what they're doing, you know, it it seems like And and maybe I'm going to a dystopian conclusion, but I've read through some of the companies that you've investigated and some of the tools that they've used.

It feels like it's becoming more and more pervasive. So first companies just looked at what you submitted to them, your resume. Then companies started scrubbing what the world knew about you. And then now because of the way data is shared.

I'm even seeing stories where they're saying some companies may be able to go, you know, as far as your social media I mean, one of the craziest examples I saw, which I don't know how true it is, is like like your Uber rating is a possibility in a future, which sounds like something China was doing or trialling by the way. Yeah, yeah. With the s with the uh uh social media score, yeah. And you get like certain benefits of society, but if you like jaywalk

Oh yeah, grandma? That's me. No, really. And and so but now when I when I think of that, I'm like w like are we heading towards a world where a company can hire you or fire you Looking at your Spotify playlist going, oh, this oh no. Oh no, yeah, yeah, yeah. I mean look, some psychologists say that like the way we behave is very predictive.

And they can certain find certain ways. Like there is a um there was a uh a a a big finding a f uh a few years ago and I think it was like uh that a lot of computer scientists are really into manga comics. Um so The question is like, well, if you look at then resumes, should you hire the people that like mangas and um because you know they're gonna be good computer scientists? But what is with the people who are great computer scientists who just are not into manga? Like that's not fair.

uh to those people, right? So like that's sort of the problem with these shortcuts. But I sort of do feel like there is a dystopian vision that like um, you know, I sort of felt like at one point I was like, wow, maybe at one point we just

not even gonna do a job interview anymore. A company will just tell you if you're hired or fired or if they don't want you based on all of the social exhaust, the data exhaust we sort of leaf around and and companies can predict who we are. Um turns out We did test the uh uh sort of personality testing that is being used on social media. It doesn't work. Um but

It's still being used. It doesn't actually stop people from using shitty technology. That's sort of the bad bad part here, right? Like these are very actually work to predict what the people are doing. It does make me think of a dystopian world though. Like just this idea that you will be hired before you've applied for a job. Yeah. I just think of like us in the year three thousand or something and Avengers pulls up

But the door opens and they're just like, Welcome to the job, Eugene. We know you better We know you better than you know yourself, soldier And you're like, What are you talking about? Yeah. But you might not even be wrong. In my conspiracy mind I'm thinking that AI tools are just a big giant facade for data harvesting. Companies know if what they are offering to the public is still viable. Learning institutions know who are the most likely candidates for them to start giving or

keep giving the courses that they're giving because we forget that higher learning institutions are just businesses as well. Oh yeah, totally. And so some of them use this kind of technology, like to find out. One way video interviews, like

Um and um yeah, I mean I think I think what fundamentally comes down to it's kind of funny what what I have learned by like bringing AI into the talent acquisition hiring space, I learned like how bad our old processes are, like job interviews, actually really bad.

Um, because you are uh it sort of filters out the people who are good about talking about doing the job. As opposed to doing the job. So we have this like confidence versus confidence problem. Like people who like come off as like confident, we often think like, Well that person Speaks so confidently about the they must be really good. It turns out like that are more often than not men. Um and that doesn't mean actually they're competent. So we sometimes complain of No Never. What? So you know.

As always, not all men, but a lot. As little old men Ooh, acting like we know more than we do. Uh come on, Hilka. Name splaining what I think this is highlighting yet again the same point again of saying that biases have gotten it this far. I've often heard people who go, If I'm in a criminal trial and I'm thinking of what kind of lawyer to get, I want someone who's

who's talkative, who's out there, who's loud. Yeah. But the person who handles my finances must be quiet, you know, reserved and frugal and they'll know how to handle my finances. You know what I'm saying? So I've we have a lot of people.

But I'm sort of a little bit more than a little bit of a little bit of a little bit of a little bit of a razzle dazzle. We've seen the lawyers that that represent razzle and it it's interesting to you to exactly what you're saying. If I hear you correctly, you're saying In a in a way, it seems like we are expanding and scaling on a foundation that was already broken.

Absolutely. Um the way we hired was already broken. Like job interviews are broken, like sort of looking at and you know, resumes have very have very little predictability'cause you know, like y you put certain like things you need to have this skill and this skill in in the job and then and and you put that everyone who applies for the job, ninety nine percent of the people will have that on their resume.

Um, so and you can't find like things like teamwork. Are you a good collaborator? Yeah, you don't know. How are you gonna know that from a resume? How are you gonna know that from a job interview? You can ask like questions like, Well, tell me how you overcome uh, you know, a really a challenging situation at work.

But you can you can train for that. Like the best way, you know, one of the best way to predict if you're gonna be successful, this will come to no surprise to anyone, is to put you in the job. Uh, and then you can find out if you're gonna be good at the job. That is yeah, totally doesn't work for most companies to hire a hundred people and then let let ninety-nine go at the end of the month.

Uh but sort of my hope sometimes is like, wait a second, like we have virtual reality, like we have other ways, like could we put people in the jobs and actually have them do the jobs, the most important parts of the jobs? Um and then figure out like that. And I think that would also give candidates a way to sort of understand better what is this job actually. Have you suggested this to companies?

I like this idea. You I really do. You I really do. I do. I mean I think I think it turns out uh you know, I do think it is a little bit more complicated than just what I'm saying. 'Cause you know, like a lot of jobs have different yeah. They have like different uh capabilities and di different things that you have to test for. Right. Um and some of that is is hard to test. But we need to be better or some like total cynics in this world have sort of suggested, you know what?

If you wanna hire, use a random number generator because that is at least fair. You have the same fair chance as you, you, and you um to to get to get uh picked. Um obviously everyone's also ways to go bankrupt as a company. I mean that's like a whew. I'm all for like r but that's also like chaos. There's random and then there's chaos. You know what I mean? If you're gonna say to people, Yeah, a random number, just bring the person in. Uh yeah, I dunno.

Oh okay, so you're going okay, so you're going basic capabilities and then like you've got the qualifications and then it's random. Oh yeah. I'm in for that. Okay, okay. Yeah. Try that down. Um wait, so but but you know what I want to move on to is like the we're talking a lot about hiring. Yeah. Your work really delves into keeping the job, which I think a lot of people aren't aware of.

And might even be more terrified to find out about Oh yeah. What we see the surveillance that we're gonna do. Yeah, like like for instance and and I I know there was an explosion of this during COVID. Wants people working remote and then companies like we need software to know whether people are actually in their underpants or not, and we need to figure out like what people are doing at home.

But now companies are starting to deploy AIs that not only see how like active you are, but they try to predict whether or not the company should fire you, not based on what you're doing now, but what the company thinks you might want to maybe do or not do I mean I think it's often like you know it's called like a um uh a digital neighbor or something. Like sort of like the the the idea is like

You were a vice president of sales of North America, so there might be a vice president of sales in in Europe. And one of them is like uh might be more um successful or not. That's actually kind of vague and hard. Um but for this the sake of this this example we'll assume, okay, maybe maybe they're the uh the European person is is better at their job. And so then an AI will like sort of take in all of the digital traces that you leave, how many emails you

send how many Zoom meetings you attend. Are you a bullying Zoom meetings? Do you speak up? Like you can kind of uh assess um a lot of different things and then tell the person in the US like Hey, the person that is your job in Europe and like sells more or whatever, like is more successful, they do this. Why aren't you doing that? It's sort of like a clone of like looking at all of their everything that gets recorded.

And um, you know, it's sort of like, I don't know, we have different ways to be successful. Like maybe you write five hundred emails, the next person is successful by doing like a hundred

uh in person meetings a week. That's probably not possible. But, you know, maybe they're doing fifty a week. Who knows? Um but we sort of and you know, what does it mean to be successful? Like we had this like whole thing Um probably don't remember this and I might be dating myself, but there used to be like algorithms in New York City to assess teachers.

Like 20 years ago or so. Like every uh parent was like, I want to know how good my teachers. Well, it turns out like these algorithms were terrible. And and a lot of P teachers were like put in rubber rooms. uh because their their students didn't gain enough knowledge in a year. Um but it could be that they were already at the top. Wait, the teachers were put in what?

They're called rubber rooms when like when like teachers were not in the classroom anymore but they were still on the payroll of the Department of Education, they called them rubber rooms of the Rubber Rooms. Yeah. Is there to go somewhere to a room made out of rubber? No, it sounds like a cell.

It Okay, no,'cause I you just went through then you like they put the teachers in rubber rooms and then I was like, Wait, they did what to them? So they just called it a rubber room. Huh. I don't actually know the history of that. Yeah, I wanna know it. Yeah, no no I'm trying if someone's taking me to a rubber room I wanna know what a rubber room is.

You would love to go to the rubber room. Oh, wow. I don't know if I want to go there. You're going to have to play for free. You don't have to do nothing. Wouldn't you want to be in a rubber room? Play squash. I still can't believe how digital peeping tom and a digital telltales

It's just everywhere now. Yeah. It is everywhere. I mean, you know, it starts like super benign with like your your green light on your email, like are you active or not? That's sort of like a way. Yeah. Um and and and and then we see when people realize, oh, everything gets gets recorded. Um we see sort of what we call productivity theater. Um, you know, that people like slow it down. Did you say productivity theatre? Yeah. It's like Like sort of gaming the algorithms.

Um so you're acting like we're busy. So like in the morning, like you check in on Slack and be like, hey everyone, good morning, like 745, crazy. And then you turn around and take your dog for a walk, and then you don't show up at your desk at 10. But Smoking screens. You were like you were productive at seven forty five. Uh well, you know, an algorithm will now be able to uh understand that you haven't said anything.

working in an office. I remember the first time and only time I worked in an office, I was always shocked by how some people We're just constantly sending emails and messages And I always felt like they were unnecessary and they were always at random times, sometimes sometimes on a weekend, some I was like, What it but now when you when you put it that way, I go, they weren't working, they were trying to maintain the appearance of working.

Productivity. So you just like yeah, you send a message at six A. M. and people are like, Man, are you up at six AM? Yeah, wow. Emails at three AM? What the um Well you just don't stop working. Yeah. And you know, I I do you think that Meanwhile you just left the club send Schedule schedule send. Schedule send. Schedule send.

Yeah. But you know, think about it like the the the office was like sort of uh always a place to look for productivity, right? Because you had a manager, look at everyone who's working and if you left early. Uh that was not so good. Um even though, you know, we know that some people just like set at their computers, surfed the internet and didn't do any work. But they were physically at their seats. We didn't have the technology to actually like sort of

see every one of their clicks and what they're doing. Um, and now we do. And sort of we can sort of uh look at everything you do. But like the question is like, is this kind of analysis really meaningful to understand how many emails you sent, does that actually have anything to do if you are productive or successful? And what does successful in this in this

Job mean. Those those computer systems you're speaking about, I remember reading about how warehouses also using it. Like this is this is something that I hope people understand will be pervasive across all jobs. In an office where you're using a computer, they can track your clicks, they can track your typing, see what you're doing and how you're doing it. But in warehouses I've seen that now they're deploying AI camera systems that see how many employees take

Bathroom breaks or don't take bathroom. I swear. How how long you spend in the bathroom, how quickly you actually move one package over to the next, how And different algorithms, how many like items do you put in a box per minute, per hour? Imagine your bladder. Your bladder is the reason that you because you've got a smaller bladder than another person, you're getting fired.

Technically that would be illegal, but Yeah, but they wouldn't say it's because of that. You take excessive bathroom breaks. Yeah. Or you have you're falling under your productivity. Exactly, because the other people around you, they're hitting these numbers. Why aren't you hitting those numbers? As a conspiracy theorist I'll always say who who Helk is benefiting from from this. Who is because because I I look at at COVID

And you explained to me how tough COVID was in the city. Yeah. But if you look around the world, how many running shoes have suddenly become in fashion? How many running clubs? How many running apps are being used? How many outdoor activities, hiking, you name it, that people are now having invested themselves in and investing a ton of money in because they missed being outside so much because it was taken away from them. Could it be That people that fund startups

are now having the time of their life because they realize there's these educated people who are trying to get into the job market with these kind of expertise and these kind of interests. But maybe they're not going to get in there. So how about we give them a hand and make money out of it? Sure. I mean, I think like the way we see like this kind of technology benefit is usually uh the companies because that's where the money is, right? Like is is an individual like gonna buy the money.

company, like ex Success AI, like we we don't really see. It's not really a market, right? Like this the same way for like job applicants, there's like there is some AI where you can sort of test your resume and the job description. Um, but we see like vastly outnumbered um AI for like vendors, uh the the the people that make the employment decisions.

those folks because that's where the money is. Like I sometimes dream of like, you know, we were talking about bias and I was like, you know, wouldn't it be cool if you have like a bias detector in job interviews that pings the hiring manager? Like, stop talking about you schooling. Like

Uh, you know, this is like where bias creeps in or at least analyze afterwards so you get like real time feedback like, Hey, you shouldn't really ask those questions. Like st stick with the structured interviews in a job interview, for example, and we don't see that because I don't think there's really a market there.

um to do that. Yet um you know, I sometimes feel like, you know, wouldn't it be cool? Like I I have a young kid. So like if you're like a parent and you have a little AI who's like, hey You really shouldn't get so upset with your kid. You should really say, I like how you did this and this. But like I think a lot of parents wouldn't want to do that because as soon as you have the data, somebody else, like Child Protective Services or wherever, can come in.

And look at that and be like the way you your kid. Yeah. No good. Like no one wants to do it. You're not fit to be a parent. We'd love to hire you as a manager at our company. How's your bladder? You have the personality to enforce the algorithms. Actually let's let's let's talk about that then.

As somebody who's investigated and gone down all of these rabbit holes, as somebody who's seen how AI is affecting who gets hired and how you get hired, who gets to stay in the job and how they get fired. Yeah. As somebody who's done all of this work, I'd I'd love to know what you think some concrete solutions could actually be, like where we see progress, where we see solutions. Is there is there something? Let let's break it down.

Is there something lawmakers can do? Is there something that companies can do? And then is there something that just workers can do? Yeah. Um so

I do think there's room for improvement in all levels. Um so I do think that there could be uh better laws here. For example, what we see, uh, you know, the funny thing is like I am originally from Germany, but Uh I remember talking to the former head of uh talent acquisition at at Vodafone, uh, which is a huge uh telecommunications company in Europe and other parts of the world, not so big in the US and

And he was laughing. He's like, You know what? Like we use AI in hiring now and and when you wanna upload your resume, there's like Germany and the rest of the world. Uh, because Germany has this one funny thing that like once you're working in a company and you have I think more than five employees, they can um

uh have a workers council. It's not a union. Sounds like it, but it's different. And the workers council there's actually a law and they get to co decide technology in the workplace. Oh so some of the surveillance technology we don't see happening in Germany because the, you know, this workers' council has to be notified. And I think a lot of companies shy away from using some of this like very intrusive

um AI tools. Um, but in the United States, for example, like anything that happens on a work computer. belongs to the company. So like don't do it like private Slack messages, like private surfing. Like all of that can be recorded by the company and it belongs to them. Yeah. Um so you want to be very careful of that. So I think there needs to be many more privacy protections and I think companies should

tell should be mandated to tell the employees what kind of software they use in them. So for example, like some of it is like very basic, but like if you suddenly print a lot, that might be an indication that you're at flight risk. So maybe the company lawyer should be looking into what you're moving away from your computer. Um, like those kinds of like sort of uh digital telltales

Um, you know, I think companies should tell us and maybe there should be a way for like uh employees to co decision making.'Come some of the time, you know, if if you're working a nuclear power plant, maybe you do want AI to scan for like exposure to radiation. I would want that. Like

Um, so you know, there might be cases where this is like actually really helpful. And and maybe everyone agrees that like, you know what, printing is a problem. You shouldn't be printing so much and you shouldn't like move files and that could be an indication that you're leaking. Um, yada, yada, yadda. Like maybe, maybe we can make a decision together, but we won't we don't see that. So it's like all top down.

Um, and people are uh, you know, these kinds of uh tools and decision makers are being used on them and they don't even know it. And I think that's really unfair and there's no way to push against that. I think also like companies need to be much more skeptical. when they buy these AI tools, not believe the hype that this is gonna solve all their problems. They're gonna hire the best people. Like actually uh uh show me, show me the evidence, like show me how it works, I'd be happy to look at it.

Um and and uh you know, I'd be open to it. Like maybe maybe an AI is better. Wouldn't that be great? But we need to know. We don't actually know that kind of stuff. So we need to interrogate these algorithms, understand the processes underneath them and really critically assess them. I think that's where maybe humans are coming in in this world. Um so we need to be uh uh uh much more skeptical there and then The applicant for jobs?

That's the hardest part.'Cause there isn't necessarily something you can do except like call your congressperson and sort of be aware what is out there and like

try some of the tools. Like, you know, there there's there's definitely better ways to like have a machine readable algorithm and there's things you can do. Yeah. Um, but you know, when like five thousand people apply for one job and they close the job description after uh you know, they they they they close the job portal after twenty four hours. Yeah.

To be be much more skeptical about these tools and put pressure on lawmakers, decision makers to do a better job here and to just be more transparent. Like one of the stories, like um of Martin, like came through because he lived uh in the European Union and knew about the laws and he asked for the data. Like there is a general privacy

uh protection law and uh you can ask for your data that companies have on you. And that's how he found out that the company used AI, which was against the law, la. So he got a he he he got a settlement, he actually started a case. So that was like a gold mine for me. I call him patient zero. Uh,'cause he's sort of the first person who like encountered these kind of AI tools in the hiring phase.

And then Ashley got the data on himself, right? That's like goal to me. Um, so we could sort of unravel and and and and talk about the case because we had the data. Um, and we don't have anything like that, at least on a federal level in the United States. So there's like way more work to be done to make this better. And I do think in general, like I do like

I think we talk a lot about like sentencing guidelines with AI to send people to prison. H should you get a mortgage? And and and I think those are all very consequential decisions and we absolutely need to take a closer look at those and look at them critically. But I also think hiring is really important too. Like it matters if I can pay the bills. Like it matters if I uh can food uh put food on the table. Like also like

happiness is tied to our jobs for many people. Like we spend enormous amounts of hours at our jobs. So like it better be something we kinda like at least. Um, so it matters if I get the job or not. So we really should be scrutinizing these kinds of system if it makes decisions on humans. If it makes decisions about my spam and it doesn't work, I'll find another spam filter. Like fine, great use for AI.

Um, but for hiring in these critical human decision makings where human lives are at stake, you gotta be much more skeptical, scrutinize these tools, um, and then we probably have a chance of building a better world. Well, I will say there's one part of the equation I'm very grateful for, and it's that we have an intrepid investigative journalist who's doing the work. Because I mean sometimes you wonder like, does my work have an impact?

I do think sometimes, you know, when I show people like my videos from eight years ago about like the emotion recognition of of facial expressions and and they're like, Wow, that could be so easily biased and I was like, Wow, I guess our work sort of like has made a difference because eight years ago we all looking at like, whoa, who knew? This is so cool. And now everyone is like

Like, oh wait a second. Like if there are only like, you know, more men than women in the data, la la yeah, and I was like, wow, there is like sort of a much more education around AI and bias and all of those things. And I think it has made an impact, slowly but surely. Slowly but surely. But I'll tell you, now I know for for my next job I've I've got something to think about. When we get out there in the streets and from my side with me on hiring. Let's do it.

And and see and see how it works. Thank you. In partnership with Sirius XM. The show is executive produced by Trevor Noah, Sanaz Yamin, and Jess Hagen. Rebecca Chain is the Our development researcher is Music, mixing and mastering. By Hannes Brown. By Ryan Harduf. Thank you so much for listening. Join me next week for another episode of What Now? Monthly treatment from moderate to severe. After an initial four month or longer dosing phase, about four months.

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Lily.com or call 1-800-Lilly RX or 1-800-545-5979. Hi, I'm Kaylin Coleman, winner of Target's HBCU Design Challenge. This challenge moved me closer to my dream of becoming a fashion designer through mentorship and support. You can find my design along with creations from other Black founders in Target's Black History.