Hilke Schellmann: Is the Algorithm Hiring the Wrong People?

[music]. 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. 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 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. >> No. >> Never. >> What? [laughter]. >> So you know. he'll come. No. [laughter]. As always, not all men, but a lot. >> Little old men acting like we know more. than we do. Us. [laughter]. >> Come on, Hila.

>> Men explaining what? [music]. [music] This is what now with Trevor. Noah. >> [music]. >> You based here? Where you based? >> Uh yeah. U based at myu training Cooper. Square. I live in Brooklyn. >> Okay. Oh, what part of Brooklyn? >> Greenpoint. >> Green Point.

>> It's a old Cish neighborhood. >> What did your voice go down when you. said Greenpoint? [laughter]. >> Greenpoint. You caught me. >> Greenpoint. >> Um well, it's very different. I've been. in the same apartment for 16 years. It's. been beautiful 16 years ago. It's still. kind of beautiful, but the neighborhood. is changing a lot. >> But is it becoming cooler and younger? >> Yeah. >> Oh, that's what you don't like about it. >> 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.

and that is really bad in Polish saying. I don't understand Polish, >> but I kind of 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 Wasau to learn. this one phrase. Um, because Polish. apparently is very hard. Um, so I kind. of like that about Greenpoint 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. [laughter] I've never understood why. people learn the phrase I can't speak. your language in another language. >> because you want to be polite. >> and I think your language a test on. yourself to see how much you can learn. >> Okay, but now think about this to think. about what this says to the other. person. >> Yeah, >> you've said to them in their language, you can't speak their language. >> 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 like being really. polite. No. Think about it. You've literally walked. up to somebody and you w 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, "That's enough for me. [laughter]. Think about it. We've got enough for. like that's enough for me." That's good. Well, um, Hila, welcome to the podcast. >> Uh, well, thank you for having me. >> Thank you so much for joining us. This. is 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 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. >> Uhhuh. >> 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 going to scan a random QR code. [laughter]. This is how your phone gets hacked. I'm. not going to scan the Q. I was just I I. just 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." Like basically like 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 well it's. changing the world of work and I saw it. 8 years ago starting and I was like oh I. don't know people are aware of this and. somebody needs to look into it and.

there's 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 u 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 factualbased. society and 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 cuz 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. >> Yeah. I mean I I covered 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, no one has ever said. that." And I was like, "Really? Well, what happened?" And he's like, "I had a. job interview by a robot with a robot.". And I was like, "What? A job interview. with a robot?" Um, he's like, "Yeah, I. you know, he had applied for a baggage. handler position at an airport and he. got a call from a robot that asked him. three questions and he was really. weirded out." This was in 2017, 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 and HR and like it's being used. everywhere and no one talks about it.". And 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 AI. 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 work but it's like societal. implication. What does this mean if we. use AI in hiring? What does 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. Mhm. >> 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. inonation of your voices to find out if. you're going to be good at a job and. like the words that you say and I was. like, "Wow, who knew that like facial. expression in in 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 emotions 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. inonation of 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 kind of like when I'm. in a job interview and I say I'm nervous. and uh sorry when I'm when I'm in job. interview and and I smile and people, you know, a facial uh emotion scanning.

algorithm would say like, "Oh yeah, she's totally happy. She's smiling." And. I'm like, "I'm nervous. I'm. [laughter] not I'm not happy in a job. interview. Who in the world has ever. been happy in a job interview?" Uh so. that's kind of 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, 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 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. the 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. speechto 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. I'm like. >> 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 texts. >> But you send them very well. >> Very [laughter] well. Sometimes I send.

pictures as well with those texts. >> And an emoji. Don't 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? >> Yeah. Um well we use it in everyday.

life. Do you have a spam filter on your. email? Nicks, [laughter]. >> I specifically said to you, [laughter]. >> 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 on. 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 an AI 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.

Yeah. >> Okay. You you have to emulate that. >> Nothing. [laughter]. you know, you uh 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 and 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 like the passion for tech. that I have, you know, and sometimes I. think if you love tech too much, you're. just focusing on like the tech side of. it 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 your book really. broke it down because one of the first. things I noticed about your writing is. AI is fundamentally going to 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 or 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 going to 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 etc. 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 because. I think a lot of times people who use AI. a lot sort of 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 um 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 maybe. my calendar, book my flight. >> Yes. 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 Aentic AI. Can it really book. the best flight for you that you want? you know, we're still working on that, but it 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 how's this. pipeline going to break of people um. doing like um early entry jobs. Um how. are they going to get the expertise and. the wherewithal to like move up if we. sort of take out the the first layer of. jobs? Um maybe you just have to 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. 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 if. we cut off that level then where does. the expertise come from because we say. upskill but then who is doing the up of. the skill? >> Yeah. Yeah. I mean I think it's like. sort of a 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 yeah 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 cuz we got more what. [music] now after this.

This episode is brought to you by our. friends at Survey Monkey. You know, Eugene, I don't know if you've ever. thought of starting a business, but. every time I think about people who. start businesses, I'm like, man, you. need a specific kind of bravery. And by. bravery, I mean terrifying uncertainty. Because think about it right now, everywhere in the world, there are. people making massive lifealtering. decisions based entirely on a vibe, just. a feeling in their gut. >> That was me when joining the podcast. >> You know what I mean? >> But what if you actually knew the truth.

when you started a business? What if you. knew your direct reports weren't just. nodding along, but actually had thoughts. that they were too polite to tell you? >> Or what if you knew that your best. customers were already halfway out the. door to a competitor? Because let's face. it, we like to say follow your heart, but in business, ignorance is a very. expensive hobby. The good news, Eugene, is that Survey Monkey takes the fear out. of asking and the doubt out of deciding. It's the difference between walking into. a meeting with a hunch and walking in. with 500 validated opinions, which.

mathematically is better for your blood. pressure. The truth might hurt for a. second, but it's cheaper than being. wrong. So, let's get you a survey and. some actual answers. Sign up for an. annual plan at surveymonkey.com/wattnow. and use the code whatnow for 2 months. free. That's 2 months free with code. whatnow at surveymonkey.com/w. whatnow. You have any questions? Is it. too late for me to run a survey about. who doesn't like you in the podcast? >> What do you mean who doesn't like you? Like like me?

>> Like you? Yes. Specifically the person. in front of me. >> I think surveys are supposed to be. anonymous so that people's feelings. don't get hurt. >> But I did type anonymous there. Well, I. mean, now I'll know that you typed it. because do do you have a problem with. the working condition? >> I haven't sent it. >> But you want to make a survey about. >> I haven't sent it. >> yet. You can [music] just use the code. what now to get two months free when you. do it. Still have two months to complain. about you. I feel like [music] this got. personal. All right, Eugene, [music] let's play a.

little game. You know, make something. fun. Two truths and a lie. >> Mhm. >> 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 [clears throat] confused. First of all, why would a world leader. own a fly? Cuz those things just come. uninvited. Secondly, lying to your. friends is not cool. There's never been.

a game. No, Eugene. 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 AT&T 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 plans, streaming, and phone. deals. Take your AT&T or T-Mobile bill. 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 2025. 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.

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. Eugene, it's not a. lie. I wouldn't lie to you. It's a game. [music]. >> Okay. I'm sorry. >> I lied. UH [laughter]. [music]. >> in in the job space actually I I would. love to know like.

>> yeah [music]. >> you've done a lot of investigating and I. want to 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 CV 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 about. >> how do you think you got here [laughter]. you think if I knew you were coming.

you'd be here. >> if I looked at your. >> this was AI [laughter]. >> you now I have to say it was like shitty. AI OR. >> [laughter]. >> THIS FREAKS ME OUT 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? Like you. already have it on LinkedIn and you just. 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 at. 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 a couple years old. I talked to Google, they get over three. million applications. IBM gets 5 million. over 5 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 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 path for AI and then you know. doing like oneway video interviews and. now we have video avatars interviewing. people. >> Just 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 read your Uh, I hear you. Uh, I've. done so many.

>> 30 seconds [laughter] ago. What are you. talking about? >> No, but I didn't know. >> me. >> Yeah. >> Yeah. >> So, like a oneway 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 strengths. and weaknesses? Why do you want this. job?" And then you tape yourself. Basically, you get like a couple minutes. to 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 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 jobs that generally have people. where people are coming in quickly and. leaving quickly like they're not going. to be there. It's not a career job. So. people are going sometimes it's a career. job turnover but it's 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 a few years ago for their summer. internship they had like over 100,000. applications. Um so 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 is these. games are supposed to find your. 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 a lot of humans. they do suck at hiring because we have. bias. We have human bias. >> 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 [snorts]. my ref 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 100,000 people applying to a job, let's be honest, I don't think there's. any human who is going to get through. those 100,000 application. 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, >> you know, you want to get them when. they're fresh. >> You want to get them when they're in the. mood? >> Yeah. Not really. >> And 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 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 they 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 want to 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 um. don't work that that well and I think. what we already know so what we know. from a survey of uh seuite leaders like. sort of leadership in companies. companies um of over 2000s in Germany, the UK and the US. Uh when they asked. them if your company uses AI tools um do. they reject qual qualified do they. reject qualified candidates? And almost. 90%. 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 antib-bias training is not. going to get out of it. Um, and you. know, we all know the shortcuts, right? If you see somebody on your resume that. they went to Harvard, you're like, "Oh, they must be smart." No, probably.

[laughter]. >> I feel like you just going down a rabbit. hole. >> This episode, Eugene Ka learns about the. world. >> [laughter]. >> She's like, "Wait, what are you telling. me?". >> But but but to 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 finds like patterns, you know, and a. pattern is a predictability, 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 patent recognition. might go the opposite direction of. innovation because it's like it's almost.

like the misfits and the mistakes are. sometimes the ones who give you the. biggest clips. Do you do you know what. I'm saying? >> 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 100,000 people from all over the. world show up at the address to put in. their resume? So, I think technology. also allowed easy access cuz 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 a 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." Then they'll end up. doing the internal process anyway. because if you're going to 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. Uh 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 think it felt very uh like. passive and and and sad for a lot of. applicants until sort of LLMs and CGP. and other AI came around where now it's. like much easier for for me as an. applicant to generate a resume. There's. actually now. >> it's AI warfare. Um, yes, it is AI for. >> I'm going to use the I'm going to use. the AI to apply for the job. They're. going to use the AI to grade me. I'm. going to use the AI to pass the grades.

Then they're going to. >> 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 what are we doing? That is that is a great question. >> That becomes the question. What are we. do? 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 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. were like, "Well, that that didn't. work." But but the the prediction is.

wrong. >> You know what I'm saying? 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. >> and 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're vibe. with you. So you find the same people.

again. Um which you know we kind of 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 in 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 a lack of diversity. already because of human bias and sort. of the vibe you know you know you know. when you 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 go to like what did you. like you like this uh you know uh sports. team yada yada yada and that chitchat. feels like very good for humans to make. a human connection but it's actually. really bad uh cuz that what brings the. bias is in because as now as a hiring. manager I'm like oh man you went to the. same school as me that'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 you 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. well AI is 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. Um but what we've seen um 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 in a company. may use their tool sometimes they bring. in lawyers and do their due diligence.

like 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, um Oh, >> those that was the Amazon story that you. wrote about. >> Yeah, the Amazon story is one of them. Pretty insidious. Um, so this was this. was like if you had the word uh woman or. women um on your resume, you got. downgraded cuz you know the the the tool. had learned over time, you know, you 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 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 use their resumes that. the machine does what it does best, it. looks for patterns and it finds out. 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 their. resumes basically. >> Exactly. So this person is less likely. to be like that person. So, we're going. to downgrade that. But this had nothing. to do with your actual qualifications.

>> Wait, did AI do that or did someone who. put the input to the AI do? >> There's no input. This was this was. Yeah, you have to think about like you. know 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. the best for here. 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, 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 another case, it was like. words like Syria and Canada. And. >> what those got you up or down? >> That got you up actually. >> Combination. You were hired. >> Yes. >> Wait, wait, wait. But [laughter] now, but now if your name is Thomas on top of.

that via Canada. >> Yeah. No. 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 um passions reading about. Syria? >> Uh, Canada. I like it. Maple syrup. [laughter] to Thomas. You know what it. is? >> Thomas, Thomas, Thomas, Thomas, Thomas. And. >> Thomas, Thomas, Thomas. And then. >> So, I think the the problem is that like. most tools are like individually.

calibrated to each company. So, >> I could only get hired at Amazon by. doing this. >> 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 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 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 uh group of. people or a company even based on what. they think the taste of the of the. population or demographic is. 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. going to work for a company and the. person sits there goes I think you'd be. great here because of 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 cuz they look like this, they sound.

like this, they talk like this, actually. they'll be great for this job." So, how. many how many of us are beneficiaries of. biases? 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 interview. or 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 us 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 actually instance, there was uh the word African-American. that was used um to weigh résumés. In. another instance, there was if you had. the word baseball on 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 zero. points for both. >> In my company, I would be [laughter]. fair. You say baseball, you say.

softball, I would detract points. >> But, do you see how it's circled back? how those. >> and this was not a baseball position. The question is like you know like. baseball but no you know you know what I. in a in a way I know this this is going. to 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 where they've made a mistake. here and they can rectify. >> 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 like working for Bloomberg or he's. like work trying to get a job at. Bloomberg or something. [laughter] And. please help me understand this cuz 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. 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 of this tech, right? Because if you want the job and you get. an email with the link saying like, "Hey, you have 48 hours. Click on this. link, play this game." What are you. going to do? You're going to do it. even. though you were like and he was like. while he was doing it he was like.

>> this is weird why is ask me question. horror movie do you want to play. [laughter] 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 100 questions about like are. you the life of the party like I rather. pump it balloons um but when you realize. wait is this the only criterium I'm. going to 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, I was like, you get like 15 seconds or so to do.

that. And I was like, what does that. have to do with the job? 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 [laughter]. maybe working at a company where it's. like suspenseful incorporating. Maybe. it's like. >> I mean I want to know what this job is. [laughter] now where somebody out there. is just like. >> 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. [laughter] to get to the next line cuz.

you can't just press return. You can't. just press come on come on and then that. boss is like you know we need we need. people who can press the space bar. >> Get me THE FASTEST SPACE BAR PRESSES IN. [laughter] 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. >> 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 space bar? And that. >> this is one of the many games that they. have to have to have to play. And you.

know 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 riskaverse you. are like what your personality is. underneath this. Um like are you. somebody who likes challenges or not? Um. I guess. >> any order that you're given. I'm sure. even the the time between you deciding. are you going to press the space button. or not actually maybe counts. You know. did you really think about this. instruction? I don't I don't know if. that counts, but I did talk to. industrial um 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.". >> Called it. >> Touch my. >> called [laughter] it. 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 such a low predictive. measure to measure how good we are going. to be in a job because it also turns out. like I can overcome things in my. personality right like I don't know if. anyone of you have like I tried 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 [laughter] like a party, like a reception with actual people I. don't know, and like us.

>> going up to them, it's like 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 to challenge myself. So I was like, I'm going to I make a game for myself. >> You just walked into parties with a. keyboard and you're like, how fast can. you hit this space bar? [laughter]. >> You win. Nice to meet you. >> Nice to meet you. I'm Hila. We can be. friends. >> This is This [laughter] is my research. Um.

>> that would be I I should have done that. That would have been much more. interesting. >> What did you The game was that I have to. approach strangers and like say you did. this for yourself. Yes. Yes. Like what. was your reward? Um. >> my reward was just like well getting to. know people and like learning about. them. >> I like this. So, this was how you. overcame it for yourself. You went, "I'm. afraid of speaking to people, so I'm. going to make it a game where I just. walk up to a stranger, speak." What. happened? >> That's what I tell my journalism. students. >> What happened when it didn't go well? >> Uh, well, I'm still here. So, I was.

afraid I was going to get decapitated, right? People are nice and they're like, "What the?" 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 not as bad as. you thought. >> 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 a woman to do. that than a man to do that. >> If I walk to into a random room then. there's a bunch of women there and I'm. like hey guys. playing a game where I'm trying to be.

[laughter] social. like stranger danger psycho. [laughter]. >> Oh man. >> But for. for a woman it's much easier. So the. biases kick kick in again. If I go to a. mid 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 going to say hi to me. >> That was a good howdy though. >> Yeah. >> You like that? >> You nailed that. You nailed that. >> I'm in. If my eyes were closed when you. walked in. >> Close your eyes now. Howdy, folks.

>> Hi. Who's that over there? >> Not bad. That was not bad. >> I'm I'm in. >> Darn. >> Once I look up, [laughter]. things might change. So you see how. biases is informing how the what the. outcome ends up being cuz even. >> but it was a bias 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. less threatening. 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 14th floor but. then 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? >> I mean look we can all wish but we know. that that's that's never going to. happen. Like we h we humans are biases. machines. Yeah. Yeah. But but now that. the but now that the machines 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 cuz cuz 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 setting, bias exists. Right now.

AI has 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. 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 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'll be. like, "OH [laughter] MY GOD, LOOK HOW. RACIST the racist AI was to use." Sorry, my friend. >> AI is a Trojan horse. You're right. [laughter]. Is there is they do do you see a world. where that's possible? >> Yeah. Yeah. I mean, I I wish companies. would would would actually look at these. uh tools more closely. I think 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 make sure they. still run and there there's less bias. like they check if there's like gender. and like very basic 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 decision 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 use to predict um if you're. going to 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. And 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. [laughter] 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 had. criteria lacrosse.

Yeah. It doesn't mean that like lacrosse. is 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 more points for. baseball and fewer points for softball? But is essentially I think as a. non-American this a same game just a. bigger field. >> Hook is on my team. Minus points for. both. >> Like how you saw pickle ball and. >> beachball. We call it beachball. Beach. >> Don't bring pickle ball to this. Please. Please. [laughter] Let's not bring.

>> Trevor doesn't want to talk about pickle. ball. >> Don't press anything. We've got more. What now? After this. You know, Eugene, I don't know about. you, man, but sometimes planning a. romantic evening is one of the most. stressful things cuz there's that. specific type of performance art that. comes with a romantic night out. You. know, it's like you're sitting in a. restaurant and you realize you've just. paid a premium to have a room of. strangers watch you eat bread while you. try and have a private conversation.

It's exhausting. You know what I mean? It is. [sighs and gasps] But I realized. recently if you want to skip the theater. and the crowds, Whole Foods Market is. the place to plan the perfect indulgent. and romantic evening at home. Picture this. You see, you skip the. extra trip to the flower shop and. instead you explore the Whole Foods. Market floral department full of. gorgeous quality flowers with large.

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>> By the way, that wasn't our date. I just. got carried away and then your hand was. there and then. >> I totally understand. But you know, like. they say, there's no free dinners. Clearly. [music]. >> No, you don't owe me anything. You We. didn't even have the dinner. I'm 41. >> [music]. >> You know what I realized speaking to you. guys about this cuz 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. cuz I [laughter] I'm I'm dis. because I've worked in retail before in. South Africa and and I've realized that. HR has always been the enforcer and the. goon of the corporation cuz 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. >> 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. 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. M. >> 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 likely is they are men. how many kids do you have far from the. job you live and what are you willing to. do for this job. >> I was going to say like that you know. like um when you think about it like how. these kinds of statistics and and and. prediction works it's it it precedes s. AI by a long time, right? Like we know. statistically that if you have a longer.

commute to your job site, you're 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. if you're going to be good. at the job. >> It only says something about your. situation. >> Yeah. And and you know, and also like. well, first of all, like there are.

people who do a 2hour 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, you also have to look like. we live in very um segregated. communities in the United States. There's historical red lining. So if you. like start taking out zip codes, you. might actually like take out huge swath. of like um African-American population. or Asian-American population to be. honest. And I think where you're from 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 ability to enforce more. >> How did you know that? I think it's. plausible deniability. um of the companies that use it and buy. it from the vendor because then they. can't be taken. You know, it' be very. hard to have a court case where you say. like, well, you knew that your tool 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 like 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 want to. 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. used 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 looked. at a 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 or however you want. to call it. And uh so in in a way they. want to save more money, have less uh. labor involved and they don't want to. like hire people who now start like. picking apart the algorithms then you. know then it might not work and what are. they going to 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. triing, by the way. >> Yeah. Yeah. With this with the. [clears throat] a social. >> Yes. Remember that we're like basically. social. Yeah. If you have a higher. social score, you get to travel and you. get like certain benefits of society. But if you're like jaywalk or you know. visit grandma. >> that's me. No, really. And so, but now.

when I when I think of that, I'm like, like, are we heading towards a world. where a company can hire you or fire you. >> looking at your Spotify playlist going, "Oh, [laughter] this. Oh, no. Oh, no.". >> Oh, 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 was. a um there was a a a big finding if 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 going to 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're just not even. going to 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 leave around and. and companies can predict who we are. Um, it turns out we did test the 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? But it doesn't. 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. >> I just think of like us in the year 3000. or something and a van just pulls up, the door opens and they're just like, "Welcome to the job, Eugene. We know you. better. WE KNOW YOU BETTER THAN know. yourself, soldier." AND YOU'RE LIKE, "WHAT are you talking about?". >> 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 high learning institutions. are just businesses as well. >> Oh yeah, totally. And some of them use. this kind of technology like 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. 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 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 about. >> no. >> never. What? [laughter]. >> So you know. >> you don't us. No. [laughter]. As always, not all men. >> Us little old men [laughter]. acting like we know more than we do. Ush. >> Come on, Hila. [laughter]. >> Men explaining what?

>> Um, >> wait. I think [laughter]. 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, 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, we [laughter] we. haven't heard about this talkative. lawyer, but I'm sort of you are someone.

who goes razledazzle. We've seen the. lawyers that that represent. Yeah. Charisma. Yeah. You want And it's. interesting to 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. >> Yes, absolutely. Um the way we hired was. already broken. Like job interviews are. broken. Like sort of looking at and you. know résumés are very have very little. predictability because you know like 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 on. everyone who applies for the job 99% 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 going to know that from a. resume? How are you going to know that. from a job interview? You can ask like. questions like well tell me how you. overcome uh you know really a. challenging situation at work and but. you can you can train for that like the. best way you know one of the best way to. predict if you're going to 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 going to be good. at the job that is yeah. >> totally doesn't work for most companies. to hire 100 people and then let let 99. 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. how they actually are at the job.

>> 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 cuz. this is I like this idea [laughter]. I really do you I really do. >> I do I mean I think it turns you know I. do think it is a little bit more. complicated than just what I'm saying. because you know like a lot of jobs have. different yeah they have like different. uh capabilities and different things. that you have to test for right. >> um and some of it 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 want to.

hire use a random number generator. because that is at least fair you have. the same fair chance as you. um to to get to get uh picked obviously. >> also way to go bankrupt as a company I. mean that's AO I'm all for like but. that's also like chaos. There's random. and there's chaos. You know what. [laughter] I mean? If you're going to. say to people random number just bring. the person in. Uh yeah I don't. >> if they have the basic capabilities. >> Okay. So you're going okay so you're.

going basic capabilities and then like. you've got the qualifications random. I'm in for that. Calm down. >> Yeah. Try that. >> I'm down. Wait. So but but you know what. I want to move on to is like the. >> we're talking a lot about hiring. >> Yes. 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 what we. see the surveillance at. >> yeah like for instance and I I know. there was an explosion of this during co. >> once people working remote and then.

companies were 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. >> Yeah. I mean, I think it's often like, you know, it's called like a um a. digital neighbor or something like sort. of like the the the ideas like you were.

a vice president of sales of North. America, so there might be a vice. president of sales 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 the 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 bully in. Zoom meetings, do you speak up? like you.

can kind of 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 500. emails the next person is successful by. doing like 100 uh inerson meetings a.

week. That's probably not possible, but. you know, maybe they do 50 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 they 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 teacher is. Well, it. turns out like these algorithms were. terrible. and and a lot of 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. at the time. >> Rubber rooms. >> Yeah. >> Cuz in my head I was like picturing a. room like. >> to go somewhere to. >> like a room made of rubber what like. what? >> No, it sounds like a cell. I think it. wasn't a cell. >> It Okay. No, cuz I You just went through. that. You like they put the teachers in. rubber rooms and then I was like, "Wait, they did what to them.".

>> So, >> well, they just called [laughter] it a. rubber room. >> Huh. >> I don't actually know the history of. that. Good question. >> Yeah. I want to know. >> You want to know about the rubber room? >> Yeah. No. No. I'm If someone's taking me. to a rubber room, I want to know what a. rubber room is. >> I would actually. >> You would love to take you to the rubber. room. >> Oh, wow. >> OH MY GOD. [laughter] I DON'T I don't I. don't know if I want to go there. >> Wow. >> You're going to get paid for free at You. don't have to do nothing at two. Wouldn't you want to be in a rubber. room? >> Play squash. >> play. in a rubber. >> place to fall in a rubber room. [laughter]. >> I still can't I still can't believe how.

digital peeping Tom and a digital. tattlet tales is 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. theater? >> Yeah, it's like like sort of gaming the. algorithms.

Um so you. >> acting like we're busy. >> Exactly. So like in the morning like you. check in on Slack and be like, "Hey. everyone, good morning." Like 7:45. Crazy. [laughter]. >> And then you turn around and take your. dog for a walk. And then you don't show. up at your desk at 10:00. But. smoke and screen Queens, you were like. you were productive at 7:45. Uh well, you know, an algorithm will now be able. to uh understand that you haven't said. anything. >> I tell you what you've just done, though. You have in a single sentence. unraveled one of the greatest mysteries.

I have struggled with 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. were just constantly sending emails and. messages. and I always felt like they were. unnecessary and they were always at. random times sometime sometimes on a. weekend some I was like what 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 you're just like yeah you. send a message at 6:00 a.m. And people. like, "Man, are you up at 6:00 a.m.?". >> Yeah. Wow. Emails at 3:00 a.m. What the. um. >> Well, you just don't stop working. >> Yeah. And you know, I I do think that. >> meanwhile you just left the club. [laughter]. >> Send. >> schedule. Oh, schedule. Look at this. >> 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. that was not so good. Um, even though. you know we know that some people just. like sat at their computer, surf 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 send? Does that. actually have anything to do if you are. productive or successful? 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 cuz if you work in an office where. you're using a computer they can track. your clicks they can track your 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? >> I 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 because they would just go like. 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 always say. who who is benefiting from. from this? Who is because I I look at at. CO and you explain to me how tough CO. 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 interest, but maybe they're not going. to get in there. So, how about we give. them a hand and make money out of them?

>> 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 going to buy. for success. >> company like 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 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 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 your 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 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 talk to your kid, no good." like no. one wants. >> You're not fit to be a parent. We'd love. to hire you as a manager at our company. [laughter]. >> How's your bladder? >> You have you have the personality. to [laughter] enforce the algorithm. >> So, 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' 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'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. So I do think. that there could be better laws here. For example, what we see 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 and. hiring now and and when you want to. 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 code decide. technology in the workplace. 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 their 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 tail tales. Um, you. know, I think companies should tell us. and maybe there should be a way for like. uh employees to co-deision-making. because some of the time, you know, if. you're working in 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 yada 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 going. to solve all their problems. I'm going. to 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 algorithm, 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 as like. the applicant for jobs, that's the.

hardest part because there isn't. necessarily something you can do except. like call your congressperson and sort. of be aware of 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. Um, but you know, when like 5,000 people. apply for one job and they close the job. description after, you know, they they. they they close the job portal after 24. hours, [sighs and gasps]. there's there's nothing we can help you. with there. Like it's, you know, it's. sort of like a bigger societal change 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. 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. because he's sort of the first person. who like encountered these kind of AI. tools in the hiring phase and then. actually got the data on himself, right? That's like gold 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. 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 can food put. food on the table. Like also like. happiness is tied to our jobs for many. people. enormous.

kind of like. 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 got to 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, >> I do. Sometimes you wonder like, does my. work have an impact? But I do think. sometimes, you know, when I show people. like my videos from 8 years ago about. like the emotion recognition of of. facial expressions and and they're like, "Wow, that could be so easily biased.". now it's like wow I guess our work sort. of like has made a difference because 8. years ago we all looking at like whoa. who knew this is so cool and now.

everyone is like oh wait a second like. if there only like you know more men. than women in the data la 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' I've got. something to think about when we get out. there in the streets. [music] And from. my side, >> please work with me on hiring. Let's do. some. >> Thank you very much, you know, from from. me um you know, from Syria, from Canada.

and Thomas, we just want to say. >> let's let's be mind on hiring and see. and see how it works. [laughter]. >> Thank you very much. >> Thank you. >> Thank you so much for watching the. episode. If you enjoyed it, [music] pass. it on to a friend. If you didn't enjoy. it, pass it on to your friend still. let. them suffer for a change. And don't. forget to engage with us in the. comments. Uh if you want to suggest a. guest, maybe there's questions you want, maybe there's [music] ideas that you. have, you can chat to us. We will read.

through the comments and we'll get into. it. Either way, I appreciate you taking. the time. Thank you for hanging out with. us on What Now. And remember, we do this. for [music] you, so we would like to. hear from you. Till next time. Thank. you.