Hilke Schellmann: Is Your New Boss a Robot?

(upbeat music) - What I have learned by bringing AI into the talent acquisition hiring space, I learned how bad our old processes are, like job interviews, actually really bad. Because 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 confidence versus confidence problem, like people who will come off as like confident, we often think like, well, that person speaks so confidently about them, it must be really good. It turns out like that are more often than not men, and that doesn't mean actually they're competent. So we sometimes complain about them. - No. - Never. - What? (laughing) - So you know. (laughing) - You don't have to shoot up. - No. - As always, not all men, but a lot. - No men, acting like we know more than we do for us. (laughing) - Come on, you'll go. (laughing) - Men's claiming what? (laughing) (upbeat music) - This is what now, with Trevor Noah. (upbeat music) (upbeat music) - You based here, where you based? - Yeah, based in NYU, training Cooper Square. - Okay, Brooklyn. - Oh, what part of Brooklyn? - Green point. - Green point. - So that's-- - That's the Polish neighborhood. - Why would you want green point? - That was a green point. (laughing) - Green point. - Yeah, you know. - Green point. - Well, it's very different. I've been in the same apartment for 16 years. Been beautiful 16 years ago. It's still kind of beautiful, but the neighborhood is changing a lot. - But isn't it becoming cooler and younger? - Yeah, that's what you don't like about it. - Well, I like that it was 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, Niemien puts on me for Polska, and that is really bad in Polish, saying I don't understand Polish. But I kind of like that. - Wait, 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, if 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 Warsaw to learn this one phrase, because Polish apparently is very hard. 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. And 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 you want to be polite. - And I think, but just speak your language. - But just speak your language. - It's a test on yourself to stop that 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 wanna 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 very polite. - No, no, no, no, no, no, no, no, no. - You're not that easy. - You wanna be nice to them. - You've literally walked up to somebody, and you, 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. (laughing) Think about it. - We've got enough of it. - You're like, no, that's enough for me. That's good. Well, Hilka, welcome to the podcast. - Well, thank you for having me. - Thank you so much for joining us. This is like, you know, sometimes, and maybe it's confirmation bias. Sometimes you'll see a thing in the world that confirms the feeling that you're having on the idea. And like a lot of us will be like, it's a sign. It's a sign. Literally coming here 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? - I'm not gonna scan a random QR code. This is how your phone gets hacked. I'm not gonna scan the QR. I was just, I just looked and I was like, yes. - I always said 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, who are the winners, and who are the losers? So like, before we delve into it, it's like, if you were to explain to somebody who you are, and what your passion is in and around the topic of AI, and how it relates to work, how would you introduce yourself to them? - Oh, wow. I guess I feel like, I'm an investigative journalist, and I used to investigate all kinds of things, and now I just investigate AI, and I'm trying to understand, how does it work in society, and maybe who are the winners and the losers? But also, I really think about changing the world of work, and I saw it eight 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 was nobody else there who was looking into it, so I was like, might as well look into it. I'm just driven by a sort of curiosity, and I'm like, what is going on here? So now it has a little bit involved. I investigate AI, not only AI and hiring, and in the world of work, I also build AI tools. I think about how journalism will be impacted by AI, and how we can maybe save journalism, or a factual based society, when everything can be generated. 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 were the other topics you were covering before this, that you drew? - Yeah, I mean, I covered violence against women in Pakistan, I went to Pakistan, and I looked at South Asia, I did all kinds of things. And I don't know, I had like one lift ride in 2017, and the fall, I was in Washington DC, trying to get from a conference that has nothing to do with AI, 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 had a weird day, and they used to leave 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? Job interview with a robot? He said, yeah, he had applied for a baggage handle 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 light years further down the road of AI now. But I was like, I've 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, they're all these AI vendors in HR, and it's being used everywhere, no one talks about it, and down the rabbit hole I went. And somehow, it never, it doesn't let me go. I'm thinking about the next four books on AI, the next research studies on AI. It just doesn't, I don't know how I, I don't know. I'm very, I'm very bad at predicting the future, 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 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 proofment as we wish it would be, at least in hiring. So I think that was a little bit surprising, sadly. 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 facial expression and job interview "could be predictive of your success at a job?" Like 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 a lot of experts who are like, "What? Emotion and faces?" Like that doesn't exist to predict how good you are at a job, and I was like, "Oh, that's too bad. "Internation 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 sorry, when I'm in a job interview, and I smile, and people, you know, facial 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. "I'm not happy in a job interview. "Who in the world have been happy in a job interview?" So that's kind of like, you know, it is a prediction, but we're using it to like sort of select people. - Yeah, 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 me, me who's standing right there, so I have to like take it on. It's like, you know, when the 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 you know, the companies use the recording, run them through a transcription service, like speech to text transcription, like you have on your phone, and then the AI predicts upon that transcription. And she was like, "I wonder how good the transcriptions "have for 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, a sociology professor. I don't do this work alone, but 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. sometimes I send pictures as well with those things and then emoji don't get me started but 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 attack 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. Well we use it in everyday life do you have a spam filter on your email? NICS! Well you know it's like sort of the rise of the 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 just thinks better than we used to where we say like oh if this then do this like we have now self learning tools that can sort of do translations from you know we could now be talking in German or French and in AI could just translate that in our voices and in AI can generate that 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 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 and I can definitely do that. Yeah, and then to speak German and then do you speak German? Do you speak German? Yeah, speak German. These things are so good get us AI. Okay you don't have to relate the AI. You know your your book your book really I think shook me up in in in the perfect ways because you've written extensively about about the world of AI and what I wanted this conversation to do because I I try to talk to people like Eugene funny enough who I realized 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 you know what I mean like like like job has constantly had like evolutions over time like people used to go like a job is this and you don't like it meant using your hands and people like that's not a job and the first people on a computer or thinking 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 your investigations on like how AI is changing what jobs actually are aren't like in different fields lawyers doctors yeah yeah I mean I think we already see some of it coming down you know we see we already see some of the consequences of like AI infiltrating our our daily lives we see a lot of way less like sort of early koea hiring because I think a lot of times people who use AI a lot sort of fad describe it as like oh yeah I have like a little interim with me who does like a lot of job homie right like they can write code for me they can do 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 write that and these will be maybe we're going to give things to yeah totally totally can do a lot of that you know we're still thinking about like still are looking into like agentic 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 help you like generate research doing math problems all kinds of things so I think 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 house this pipeline gonna break of people doing like early entry jobs 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 yeah just like upscale 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 like your job is just 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 yeah because we say upscale but then who is doing the up of the skill yeah yeah I mean I think it's like sort of and you know when I sometimes fundamentally think of and you know we don't have all the answers yet just some of these questions if I may say that it's like sort of like what what stays as a human in the age of AI right if like AI can do sort of what we think is 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 because we got more what now after this in in the job space actually I would love to know like yeah you've done a lot of investigating and I want to get into some of the stories because 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 like you got here you think if I knew you were coming you'd be here if I looked at you this was AI you know I have to say it was like shitty yeah this freaks me out every 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 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 open 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 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 going on the yes per for AI and then you know doing like one-way video interviews and now we have video avatars interviewing people just just breakdown what is a one-way video interview because I think a lot of people I didn't know what that was until I told me yeah I hear uh I've done so many I'm 30 seconds ago what are you talking about no but I didn't know me yeah so like a one-way video audio and review like uh you know there's now a traditional way to do this which is like six or seven years old where you don't have anybody else uh on like you know you kind of lock in you get a link um do this uh video and review 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 ex we 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 of prepare and then you tape yourself like saying like my strengths and my weaknesses this uh and then I think all of the uh applicants I've spoken to would think that like a human watches all these videos bless their hearts if they do and some companies actually do have humans watching all of these but some companies also use AI uh to uh rang 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 a job generally where people where people are coming in quickly and leaving quickly like they're not going to be the it's not a career job so people are going sometimes it's a career job but it's just like high turnover uh 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 if a few years ago for the summer internship they had like over 100,000 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 these games are supposed to find your personality um while you're clicking on balloons pumping up balloons definitely personality all kinds of ways to assess you without maybe putting in a whole lot of effort because humans are expensive to do this work and also sorry to say this but at a lot of humans they do suck at hiring because so that's yeah we have human bias but that's so that's a conundrum 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 reflex when I hear something like that is to go oh no this is this is not good how can you have a eyes 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's 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 know you want to get them when they're fresh you want to get them when they're in the mood yeah no and I wonder is there a world where like does the AI make it better than you know I wish I could tell you that so we don't know we don't know I've asked many many companies to let me come in as a researcher and like sort of look at like here's your traditional way of hiring here's your AI hiring and have this like fun at both times and then sort of double check like you know the people that they I said that would be high performance 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 don't work that that well and I think what we already know so what we know from a survey of C-suite leaders like sort of leadership and companies of over two thousand in Germany the UK and the US when they asked them if your company uses AI tools do they reject quality that qualified do they reject qualified candidates and almost 90% of the leadership said yes so they know that their tools reject qualified candidates they still use it because I guess the efficiency from using AI versus humans it's just much much more greater but it's not that we know that one process is better than the other I mean we do know that like humans are very biased and hiring and even the best anti-biased training is not going to get out of it yeah and you know we all know the shortcuts why if you see somebody on your resume that they went to Harvard you're like oh they must be smart no they're not please she's like we know for so short time this episode you're Jim Kaza learns about the world she's like wait what are you telling me but but you go you know so this is this is this is this is where 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 as far as it finds like patterns and you know yeah 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 yeah the outlier so I wonder if companies in moving all of their resources towards efficiency and pattern recognition might go the opposite direction of innovation because it's like it's almost like the misfits and the mistakes but sometimes the ones who give you the biggest things yeah yeah yeah totally I feel like the solution has caused a problem well we're speaking about how many people had applied to Goldman Sachs and I think if it wasn't for technology would you still get that many applications that's interesting yeah 100,000 people from all over the world show up at the address to put in their resumes I think technology also allowed easy access absolutely there's people who know they don't qualify but would do it anyway yeah why would you put a human through all of this but also I think it's a so 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 money's and files and information you'd want someone that you know so I think companies know exactly what's going on but they just sending out hope and I think once you advertise a job it's a great way to advertise your company as well 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 you know I think I think it felt very like passive and and and sad for a lot of applicants until sort of LLMS and chat GPT another AI camera hardware now it's like much easier for for me as an applicant to generate a resume there's actually now yes it is 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 post the grades exactly I mean try to use AI to like outsmart the AI there's actually AI programs that now apply for you and so you don't even have to do anything and 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 that is a great question 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 you that they considered correct for the company but a lot of the time it's just wrong yeah 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 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'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 just a vibe check it was a vibe check yeah but it turns out like vibe checks not so great actually like because you are predicting who's a good employee yeah but but also like a vibe check it's like finding people who like like are often like have the same background as you they speak like you they're vibe with you exactly so you find the same people again yes but you know we kind of know that like diversity is good for companies also like I mean I think that's why we have you know fewer women people of color and leadership positions because we have underestimated them as humans and hiring for decades and and promotion decisions so we have like sort of a lack of diversity already because of human bias and sort of the vibe 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 all what school did you do like what did you you like this you like this you know sports team yada yada yada and that chitchat feels like very good for humans to make a human connection yes but it's actually really bad uh because that brings the bias and because as and I was 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 you 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 we'll just look at your like capabilities your skills and find the most skilled person um but what we've seen um in some of the AI tools when I talk to 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 tools 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 uh those the Amazon those the Amazon story that you wrote about yeah the Amazon story is one of them but um so this was this was like if you had the word uh woman or women yes um on your resume you got downgraded because you know the the the tool had learned over time you know you you you give it um uh uh resumes of people who currently work here or or you 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 were in the role used their resumes that the 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 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 it's basically 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 no AI didn't put this was this isn't really true yeah you have to think about like you know sort of uh present AI what we do is like we give uh at 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 this person yeah so yeah 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 the 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 have 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 If you had the combination you are hired. Yes Canada So here's my question though. Does that mean that people could or the tricks that people could use now so if I was writing a resume today Could I just write somewhere randomly? Syria can have questions reading about Syria Canada, I like it Thomas Thomas you know what it is Thomas Thomas Thomas Thomas Thomas Thomas Thomas Thomas Thomas and then well 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 But they say they changed that they also say that their Machine learning algorithm was never used solely to make hiring decisions 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, this was not the only thing. This was merely a pilot program that determined you know the more you guys talk to my realize Are we are you 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 So you think in generally or in the hiring process not in the hiring process because if you're gonna work for a company and the person says they 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 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 many of us are beneficiaries of biases? I think I think a lot of us are beneficiaries and a lot of us also have Been sort of the victims of bias and probably unbeknownst because you know you go in for for a job in a viewer You send in your resume and most likely is to get rejected right because there's only so many jobs at the at the That are being given out 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 Oh, I might have been that you've been rejected because your name is Thomas or in one actually incense. There was The word African-American that was used To weigh resumes and 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. Yeah, so That's probably gender discrimination. I would give you zero points for both In my company I'd be fair you say baseball you say softball. I would detract the points For sure, but do you see how it circled back how those and this was not a baseball position You know what the question is like you know, yeah, there's nothing in baseball But now you know you know one I whoa in a way I know this this is gonna sound like yeah 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 always like working trying to get a job at Bloomberg or something And please help me understand this because 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, but he did apply to a job. Okay He was based in Barcelona and and play and he was based in Barcelona and applied to a job in in London And he got a link immediately after applying saying like hey go to this link And you know I sort of feel like we as a job applicants We are sort of forced consumers of this tech right because if you want the job and you get an email with the links saying like Hey, you have 48 hours click on this link. Yeah play this game. What are you gonna do? You're gonna do it Even though you were like and he was like while he was doing it He was like, this is where your wife's last name sounds like you have a horror movie 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 a rather pump it balloons But when you realize wait, is this the only criteria? I'm gonna be judged on how well I like pump balloons or like And and and one of the games I had to hit the space bar as fast as possible and while I was doing that You get like 15 seconds or so to do that and I was like what does it have to do with the job like and what jobs 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 looking at a company with like suspenseful pause incorporates and it's like I mean, I want to know what this job is 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 To get to the next line because you can't just press return Well come on come on and then that boss was like, you know, we need we need people to impress the space bar Get me the fastest space bar We found them we found them But you know, I mean, it's interesting like that actually that sweet sweet 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 space bar and this is one of the major games that you 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 you know how risk reverse you are like what your personality is underneath this Like I use somebody who likes challenges or not I guess space bars any order that you're given I'm sure even the time between you deciding are you gonna press the space button or not Actually, maybe counts, you know, that's you really think about this instruction I don't know if that counts but I did talk to and thus feels like industrial organizational psychologists Who said yeah, we looked at all of those things and actually the people that take longer Until they start playing that actually less successful, but he said we are not using that Criteria cold You did call it yeah, 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 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 I tried to you know how 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 was like oh 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 gonna I'm making a game for myself Walked into parties with a keyboard and you're like how fast can you hit this space bar? You went We can be friends That would be I should have done that. That would have been much more interesting What did you what did you know that the game was that I have to approach strangers and like say hi Yes, yes My reward was just like while 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 gonna make it a game where I just walk up to a stranger. Yeah, speak what happens with this? But 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 gonna get decapitated right people are nice. They're 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 like Yes, but you see this is AI again 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 random room and there's a bunch of women, then I'm like hey guys I should take a game where I'm trying to be so sure It's a dangerous cycle But for women So for women is much easier so the bias is kicking again If I go to a midwest town as a black man from Africa and I walk in them there's truckers and I go howdy folks No one's gonna say hi to me. That was a good howdy. Yeah You like that you nailed that I nailed that. I mean It's my eyes were closed when you walked in close your eyes now howdy folks. Hey, who's not bad. That was not bad I'm in darn once I look up Things might change so you see how biases is informing how the what the outcome ends up being Well, but it wasn't 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 They were on the other on the receiving side of it. They were like, here's a woman. She's smart. She's nice. Let's let's let's straight. 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 14th floor. But then, but then that said nice. 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 he we humans are biases machines. Yeah, but now that the but now that the machines right, 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 because here's here's what I think about 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 that we know that social setting social sits bias exists, right? Now AI has gotten involved. And we see the AI mirroring many of our biases. 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 we had 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 woman or because they said black, but now you can you can actually look at the data and go, oh, damn, and I sometimes wonder if it'll be easier and again, this could be the optimistic side of me, but 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. The racist, the racist AI was to use. Sorry, my eyes are Trojan horse. You're right. Is the, is the, do you, do you see a world where that's possible? Yeah, I mean, I, I wish companies would would would actually look at these tools more closely. I think that the general notion though is they buy it from a vendor, the vendor sort of like, you know, sort of services, the algorithm over time and make sure they still run. And there's less bias like the check if there's like gender and like very basic racial bias in there, but they never look at like, you know, doesn't let people with disabilities do something like that, right? Like, and it also we don't see a whole lot of companies actually checking how are the decision being made. 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 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 skills and your capabilities and not if you are on the baseball team or not, but, 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 they, I found something that humans couldn't that like in this case it was playing lacrosse in high school. That was like a predictive 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. I found this like hidden gem that we humans couldn't and the lawyer started laughing. And he was like, God, you think like a human. It's a pattern machine. It does a statistical analysis for whatever reason, like playing lacrosse in high school. A bunch of people who were in the job. 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 with all the other team sports, like why weren't they included? Why do you get more points for baseball and fewer points for a softball? It is essentially I think is a non-American is a same game, just a bigger field. He'll cause on my team, minus points for both, like how you saw a pickleball and. Oh, a beach ball. We call it beach ball. Don't bring pickleball to this, please. Don't bring. Trevor doesn't want to talk about pickleball. Don't press anything. We've got more what now after this. You know what I realized speaking to you guys about this? Because I wanted to know as little as possible about the topic so I can get enlightened in real time is company. How does that go? Very well. Because I've worked in retail before in South Africa. And I've realized that HR has always been the enforcer and the goon of the corporation. Because 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 when 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 that 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 blood and blood and blood and blood. And then the people that say those words maybe they get hired because likely hood is they are men. How many kids do you have? How far from the job you live and what are you willing to do for this job? I was going to say when you think about it, how these kinds of statistics and prediction works, it precedes AI by a long time. 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? We've seen companies trying to use this like zip codes and stuff to then say like okay well we only hire the people that are living in the zip code right around our store location because they are less likely to quit. But that's a criteria that has nothing to do with the job. It doesn't say anything about your capability isn't if you're going to be good at the job. It doesn't say about your situation. First of all, there are people who do 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. And then on the other hand, you also have to look like we live in very segregated communities in the United States. There's a historical vet lining. So if you start taking out zip codes, you might actually take out huge swath of African American population or Asian American population. Yeah, and I think that's a real problem. And we sort of see this kind of 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 say, Oh my god. How did you know that? I think it's plausible deniability of the companies that use it and buy from the vendor because then they can be taken. And you know, it'd be very hard to have a court case where you say like, well, you knew that you two was biasing women. And there's like two million people that apply to this two million women that apply to this company. Yeah. And you use the bias algorithm on them. So suddenly you have your like potentially two million claims. 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. And 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 have the same questions. We found the same things that you found. And 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. But we need to learn like we'll never get better. We never we can put pressure on the vendors to build better tools if we don't know how the tools work. And if there's any problems in the tool, I just looked at the 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 lawyers who like work in the space. But I have just a sliver of the whole sort of world out there. Like we need to do a whole lot more. But I don't think it's in the company's interest. They want something that you know, like sort of saves the money in HR. It's always a cost center. HR never generates money or tell an acquisition however you want to call it. And so in a way, they want to save more money, have less labor involved. And they don't want to like hire people and I will start like picking apart the algorithms then, you know, they might not work. And what are they going to do then? They just spend so much money in it. So when you look at what they're doing, you know, it seems like 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 your Uber rating is a possibility in the future, which sounds like something China was doing or trialing by the way. Yeah, with this, with the social. Yes, remember that we're like basically, yeah, if you have a high social score, you get to travel and you get like certain benefits of society. But if you're like, Jaywalk, you know, you're like, grandma, that's me. No, really. But now when I think of that, I'm 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, 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, there was a, a big finding of a few years ago, and I think it was like that a lot of computer scientists are really into manga comics. And so the question is like, well, if you look at then resumes, should you hire the people that like mongers and 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 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, 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 companies can predict who we are. Turns out we did test the sort of personality testing that is being used on social media. It doesn't work, but it's still being used. It doesn't actually stop people from using shitty technology. That's sort of the bad part here, right? But it does 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 3,000 or something and a van just pulls up the door opens and they're just like, "Welcome to the job, Eugene, we know you've been a job, we know you've been a job, so job." 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're 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 to fund out one way video and abuse. And yeah, I mean, I think what fundamentally comes down to it's kind of funny 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. 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 competence versus confidence problem. Like people who will like come off as like confident, we often think like, "Well, that person speaks so confidently about them, it must be really good." It turns out like that are more often than not men. And that doesn't mean actually they're competent. So we sometimes complain of them. No. Never. What? So you know, you know, you know, as you know, as always, not all men to a lot. As you know, men acting like we know more than we do, come on, you'll go, man, explaining what? Wait, I think it was highlighting yet again, the same point again of saying that biases have gotten at 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 out on someone 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 haven't heard about this talking of lawyer, but I'm sort of like, you are someone who goes, "Razzle Dazzle, we'll see the lawyers that represent Rappers and the charisma." Yeah, charisma. You want, and it's interesting to exactly what you're saying. If I hear you correctly, you're saying, "In a way, it seems like we are expanding and scaling on a foundation that was already broken." Yes. Absolutely. The way we hired was already broken, like job interviews are broken, like sort of looking at, and you know, resumes have very little predictability, because you know, like you put certain things, you need to have this skill and this skill in the job, and then you put that, everyone who applies for the job, 99% of the people will have that on their resume. 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 questions like, "Well, tell me how you overcome, you know, really a challenging situation at work," but 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 for anyone, is to put you in the job, and then you can find out if you're going to be good at the job. That is, yeah. Look at that. Totally. Doesn't work for most companies to hire 100 people, and then let 99 go at the end of the month. 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, and then figure out how they actually are at the job. And I think they would also give candidates a way to sort of understand better what is this job actually. Because you suggested this to companies, because this is, I like this idea. I really do. I really do. I do think it turns out, 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 different capabilities and different things that you have to test for, and some of that 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? You don't want to hire, use a random number generator, because that is at least fair. You have the same fair chance as you and you and you to get to get picked. Yeah, it's also way to go bankrupt as a company. I mean, that's like a, I'm all for like rent, but that's also like chaos. There's random and there's chaos, you know what I mean? If you're going to say to people, yeah, random number, just bring the person in. Yeah. I don't have the basic capabilities. Okay, so you're going basic capabilities, and then like you've got a lot of applications in this random. Yeah, I'm in for that. Okay, I'm down. I'm down. Wait, so 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 are aware of and might even be more terrified to find out about. Oh, yeah. What we see at this event. Yeah, like for instance, and I know there was an explosion of this during COVID, once people were 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. Yeah. It's often like, you know, it's called like a digital neighbor or something like sort of like 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 might be more successful or not that's actually kind of vague and hard. But for this sake of this, this example, we'll assume, okay, maybe, maybe the European person is better at their job. And so then in 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 Boolean Zoom meeting? So you speak up, like it can kind of assess 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 it's more successful, they do this. Why aren't you doing that sort of like a clone of like looking at all of their everything that gets recorded and you know, it's sort of like, I don't know, we have different ways to be successful. Like maybe you might find any emails and next person is successful by doing like 100 in person meetings a week. That's probably not possible. But you know, maybe they do 50 week. Who knows? But we sort of, and you know, what does it mean to be successful? Like we have this like whole thing, 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. So like every parent was like, I want to know my teacher is, well, it turns out like these algorithms were terrible. And a lot of teachers were like put in rubber rooms because their students didn't gain enough knowledge in a year, but it could be that they were already at the top. Wait, the teachers were put in what? They called the 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 top. I don't think that we did this, like picturing a room, like I was like, I was like, go somewhere to work. Like a room in a rubber. What? No, it sounds like a cell. I think it wasn't a cell. Okay. No, because 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. Good question. Yeah. You want to know about the rubber rooms? Yeah, no, no. I'm trying. If someone's taking me to a rubber room, I want to know what a rubber room is. Oh, actually, you would love to go to the rubber room. Oh, wow. I don't know. I don't. I don't know if I want to go there. Wow. You're going to get paid for free. You don't have to do nothing. It too. Why don't you want to be in a rubber room? Play Squash. Play. Play in a rubber room. I still can't believe how digital peeping taught. and the digital tells tales is just everywhere now. - Yeah, it is everywhere. I mean, you know, it starts like super benign with like your green light on your email, like are you active or not? That's sort of like a way. - Yeah. - And then we see when people realize, oh, everything gets recorded, we see sort of what we call productivity theater, you know, that people like- - Load down. Do you say productivity theater? - Yeah. It's like sort of gaming the algorithms. - So you're like 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, 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 7.45. Well, you know, an algorithm will now be able to understand that you haven't said anything else in an hour. - Can I tell you what you've just done though, you have in a single sentence unrevelled 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, sometimes on a weekend, and I was like, what do- But now 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 6 a.m., and people are like, man, you're up at 6 a.m.? - Yeah, wow. - E-mail's at 3 a.m., what the? - Well, you just don't stop working. - Yeah, and you know, I do think that- - Then while you just left the club. (laughing) - Schedule, schedule, schedule, schedule, schedule, schedule, look at this. - Yeah, but you know, think about like the office was like sort of always a place to look for productivity, right? Because you had a manager, look at everyone who's working, and if he left early, that was not so good. Even though, you know, we know that some people just like set at their computer, so if the internet didn't do any work, but they were physically at their seats. We didn't have the technology to actually sort of see every one of their clicks and what they're doing, and now we do, and sort of we can sort of look at everything you do. But the question is like, is this kind of analysis really meaningful to understand how many emails you sent us that actually have anything to do if you are productive or successful? - Right, it's difficult. - We're just successful in this job, me. - Those computer systems you're speaking about, I remember reading about how warehouses also using it. Like this is something that I hope people understand will be pervasive across all jobs, 'cause if you work 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 long you spend in the bathroom, how quickly you actually move one package over to the next, how-- - Yeah, look at that. - They have different algorithms, how many of like items do you put in a box per minute for hours, or do you bother others? - Your bladder is the reason that, 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 now. - 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 help is benefiting from this? Who is, because I look at COVID and you explain to me how tough COVID was in the city, 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 gonna get in there, so how about we give them a hand and make money out of them? - Sure. I mean, I think the way we see this kind of technology benefit is usually the companies, because that's where the money is, right? Like it's an individual like gonna buy success company, like success AI, we don't really see it, it's not really a market, right? The same way for like job applicants, there is some AI where you can sort of test your resume in the job description, but we see like vastly outnumbered AI for like vendors, 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, you know, this is like where bias creeps, I know 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 to do that yet. You know, I sometimes feel like, you know, wouldn't it be cool, like 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, shall protect services or wherever. - Yeah, they show up. - And look at that and be like, the way you talk to your kid, you know good. Like no one wants that, right? - You're not fit to be a parent. We'd love to hire you as a manager at our company. (laughing) How's your bladder? (laughing) - You have the personality. (laughing) Don't force the algorithm. - So actually, 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 fired, the job and how they get fired. - Yeah, as somebody who's done all of this work, 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 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, so I do think there's room for improvement in all levels. So I do think that there could be better loss here. For example, what we see, you know, the funny thing is like, I am originally from Germany, but I remember talking to the former head of talent acquisition at Vodafone, which is a huge telecommunications company in Europe and other parts of the world, not so big in the US. And he was laughing, he's like, you know what? Like we use AI and hiring now, and when you want to upload your resume, there's like Germany and the rest of the world. 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 have a workers' council. It's not a union, sounds like it is different. And the workers' council, there's actually a law, and they get to co-decide technology in the workplace. So some of the surveillance technology, you don't see happening in Germany 'cause this workers' council has to be notified, and I think a lot of companies shy away from using some of this very intrusive AI tools. But in the United States, for example, like anything that happens on a work computer belongs to the company. So 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. So you want to be very careful of that. So I think they need 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 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. Like those kinds of sort of digital tales, you know, I think companies should tell us, and maybe they should be away for like employees to co-decision making. 'Cause some of the time, you know, if you're working in a nuclear problem, maybe you do one day, I just scan for like exposure to radiation, and I won't want that. Like, so you know, there might be cases where this is like actually really helpful, 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. Yeah, yeah, yeah, yeah, yeah. Like maybe we can make a decision together, but we don't see that. So it's like all tough down. And people are, you know, these kinds of tools and decision makers are being used to 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 show me, show me the evidence. Like show me how it works, I'd be happy to look at it, and you know, I'd be open to it, like maybe an AI is better. When 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. So we need to be much more skeptical there. And then as like the applicant for jobs, that's the hardest part, [BLANK_AUDIO] 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's definitely better ways to like have a machine readable algorithm and there's things you can do but you know when like 5,000 people apply for one job and they close the job description after you know that they close the job portal after 24 hours yeah 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 a 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 of Martin like came through because he lived in the European Union and knew about the laws and he asked for the data like there is a general privacy protection law and 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 law law so he got he he got a settlement he actually started a case so that was like a gold mine for me I call him patient zero because you 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 so we could sort of unravel and and and talk about the case because we have the data 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 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 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 kind of like at least 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 but for hiring in these critical human decision makings where human lives are at stake got to be much more skeptical scrutinize these tools 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 you 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 eight years ago about like the emotional recognition 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 not everyone is like oh wait a second like if they're only like you know more men than women in the data a little yet 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 truly but I'll tell you now I know for for my next job I've got something to think about when we get out there in the streets and from my side please work with me on hiring let's do like that no thank you very much you know from from me you know from Syria from Canada and Thomas we just want to say let's thank you very much let's do it and see how it works thank you very much what now with Trevor Noah is produced by day zero productions in partnership with Sirius XM the show is executive produced by Trevor Noah Sanasiamin and Jess Hackel Rebecca Chain is our producer our development researcher is Marcia Robiou music mixing and mastering by Hannes Brown random other stuff by Ryan Hadooth thank you so much for listening join me next week for another episode of what now