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This is On Point. I'm Meghna Chakrabarty. My name is Kendiana Collin. I live in Columbus, Ohio. I currently attend The Ohio State University, majoring in English on the pre-law track. I'm expected to go to law school after I finish up my senior year, which is the year after this year. So that means right now Kendiana is looking for a summer job. Just something easy and entry-level so she can earn some extra money this summer.

A few weeks ago, she applied for a sales associate job at a gym called Stretch Lab, and someone texted her to schedule an interview. Then he sent a whole paragraph telling me that it was basically going to be an AI bot that's going to interview me and make sure that I look professional and I prepare, you know, just treat it as a real interview, as if you're talking to a real person. Why couldn't I just have talked to a real person? I don't know.

Kendiana says this was her first time hearing about AI conducting interviews, but she wasn't opposed to the idea. She thought it'd be like talking to Siri on her phone. But once the interview started, things went a little sideways. It was going okay, I guess, for the first two questions. They weren't deep questions like what's the meaning of life or something. It was just, you know, what's your name, your work experience, and then asking

after the third one she just started saying vertical bar pilates over and over again vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates vertical bar pilates

We can move past this. You were just working. It was just very weird. It was very creepy. It wasn't because she was just saying it over and over again. It was the way that she was saying it. It was like mimicking human nature. Like it was a tongue twister or at one point she was laughing. I didn't record that part. And then she was like taking deep breaths and all sorts of things.

I was like, uh-uh. And I was like, no one's going to believe me if I don't record this. Kendiana, right there with you with the uh-uh. Well, she was told the interview would be sent to a manager for review, but it's been a few weeks and Kendiana has not yet heard back from the company. She's not too worried, though. She says the AI interview was actually a red flag for her and she's going to look elsewhere for summer opportunities.

Now, Candiana's experience isn't uncommon. According to the World Economic Forum, 88% of companies already use AI for initial candidate screening. And Resume Builder, a company that helps workers in the job application process, it recently did a survey of some 900 firms, and it found that about 23% of those firms already use AI to conduct interviews like what Candiana experienced.

Now, on the other side of the job search, job hunters are also using AI to seek out companies that might be the right fit to craft resumes and cover letters and specifically to find ways to shape those documents to satisfy what the AI on the other end might be looking for.

Now, each week, we seem to discover new ways in which artificial intelligence is completely shaping how we live. And today, we're going to talk about how it's changing how we look for work and how employers look for workers.

Hilke Schellman joins us to help us with that. She's an investigative reporter and assistant professor of journalism at New York University. And she's author of The Algorithm, How AI Decides Who Gets Hired, Monitored, Promoted, and Fired, and Why We Need to Fight Back. Professor Schellman, welcome to On Point. Thank you.

Hello. How are you doing? Good. I'm also going to assure you right now that I am not an AI interviewer. But you know what? Those are actually things I'm thinking about. As a journalism professor, you know, how are we going to make sure that the person on Zoom, on a call, is actually a person, right? And that's exactly what's happening in the job world as well. Well, exactly. And honestly, I say that with more frequency these days on the show because one day they will just

Kind of broom me off the stage here and replace a lot of we in the radio with AI. Some listeners might want that data to come sooner rather than later, but let's get to the job market. We found the numbers to be a little bit squishy in terms of determining how prevalent AI is in the job search or job marketplace. How would you describe that?

Yeah. I mean, it's hard to tally, right? Because, you know, no company has to tell a government regulator. We don't have like official statistics or anything. But we have surveys and we have companies that sort of self-disclose. And I've talked to a lot of companies while I was writing the book. So I talked to all of the, you know, I think the first point that I think a lot of job seekers will understand

sort of encounter AI if they, you know, it's not clear if they will sort of understand that they're encountering AI, but if you go to any of the big job platforms, Indeed, LinkedIn, Monsters, ZipRecruiter, you know, you have it. All of those companies use some form of AI, right? Like, you know, that doesn't really

mean anything, but that is a big encounter. And then we know that a lot of, especially large companies, they use applicant tracking systems, which, you know, used to be like sort of glorified spreadsheets where you would track like, okay, this person applied, we interviewed them, they've been rejected, they went to the next round. And all of those, you know, the biggest vendors of those also now have AI capabilities built in. We don't exactly know, like, does company A turn them on on

on, but we see AI being used there. We're seeing it used in video interviews, audio interviews. We see it used in assessments. There's games that we ask job seekers to play. And then companies also use it in AI background check. They can use AI to check your social media history, all of those things. So we see it all along the hiring funnel.

Do job seekers see it? Not necessarily, right? Like, I mean, if your avatar starts talking about vertical bar pilates, you know something is up, right? But like, if you send in your resume, what do you know what's going to happen to it, right? Like, you wouldn't know that AI is screening it. Yeah. Well, I just want to get into the nitty gritty about how AI is being used at each one of the levels that you just described. So let's just listen quickly to Elizabeth Senna.

She explains why employers might be looking to AI for help with the hiring process. She works at Yale as a career coach and also helps companies with recruitment.

And she gave us an example of how using AI was very successful. I just did a search for a startup, tiny startup, Series A, 17 people. They had a director of growth marketing open role. It was 1,400 applications for one job. They have no manpower to do that. So what we did is we used AI to develop a competency grid. We said, here are the five things we absolutely want.

evaluate all these resumes against these five. We took that list from 500 or whatever it was to 80. And then I eyeballed the 80.

After we were done with the process, I went back and looked at the rest because I wanted to make sure I hadn't missed anyone that AI could have missed. And it actually did a pretty good job. So, Professor Shulman, AI in any application in any sector that we've thought about, one of the things it does brilliantly is exactly this. I mean, it takes a process that might have taken weeks and can cut it down to days.

In the jobs recruitment space, that seems to be a very powerful and positive use for the technology.

Yeah, I mean, it makes hiring much more efficient, right? And it will, like, you know, analyze everyone's resume. And, you know, it doesn't have the problem of humans, right? That we are like, you know, if I'm hungry, I'm more grouchy. And maybe I look at candidates more negatively. It's a vast promise that AI vendors come out that they say, you know, this is like going to be more efficient. You can cut labor costs. And we've seen some companies have cut down their HR departments or their talent acquisitions departments, right?

It will be without bias and it will find the most well-qualified candidates. And it's true, it makes it more efficient, saves a lot of money, you hire people faster. But we really haven't seen a lot of evidence that the tools find the most qualified candidates and that there is no bias. In fact, when I started looking into it, I found a lot of bias in some of these systems. We're going to talk about that in detail because it comes up over and over again with anything regarding artificial intelligence models today.

But in terms of finding the most qualified candidates, you said that there isn't much evidence that it's successful in doing that? No.

Yeah, exactly. I think job seekers have intuitively thought that, that they felt like, "Wait, I've applied to these jobs where I was really overqualified or I was really qualified. Why didn't I get a callback?" But it's just a hunch. But we know from a survey from Harvard Business School, Professor Joe Fuller asked over 2,000 C-suite leaders in Germany, the US, and the UK. When those companies use the ITools, they asked, "Does your system reject qualified candidates?" Almost 90 percent said yes.

So the leaders themselves, leadership companies know, they know themselves that their AI tools do reject qualified candidates. Did you ask them about the sort of give and take between the efficiencies they gain, which ultimately converts into money, right? Versus maybe missing some qualified candidates.

Exactly, exactly. So, you know, it is still like amplifies the efficiency so much more for companies. And you have to like, you know, I mean, this one little startup, right, got 1400 applications. But we see, I mean, you know, when I talked to Google a few years back, they said they get over 3 million applications. IBM gets over 5 million. That's a few years back. So with generative AI, we see the volume has gone up at least 50% or so. So you can assume that for every open job, they get hundreds of thousands of applications sometimes.

Even Goldman Sachs said for their summer internship program, they got over 220,000 applications one summer. The scale is just so much that the companies feel like, you know what, if some qualified candidates are being rejected, it sucks. But we just can't have humans go through all of this. That trumps everything. Well, here's another place in which

AI is meant to help things get more efficient, but it's turning into an arms race because, as you said, with generative AI, workers are just like, I'm going to apply to 150 different places. Yeah, exactly. You know, and it's sort of funny that, like, you know, we hear job seekers online. You know, a couple of years ago, they were really frustrated because they felt like, oh, we're sending all these applications into a black hole and never hear back. And I think they feel a little bit empowered by generative AI.

now that they can craft and generate cover letters and resumes. And, you know, people joke about it. It's like AI versus AI, may the best AI win. Because, you know, everyone knows that companies also use AI, especially in the early stages of hiring, right? We see a lot of companies use AI for rejections. And when there is a, you know, a small candidate pool, when you have like maybe 10, 20, 30 people left,

You bring in humans to do the hiring, right? That hasn't still gone away. But the rejection we have sort of often outsourced to software. Well, we are speaking with Professor Hilke Schellman about how artificial intelligence is changing how we hire new workers and how workers seek jobs. And we'll have a lot more in a moment. This is On Point. On Point.

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We heard from a lot of listeners with their own AI job hiring or job search stories. So, well, actually, one of them is a person we reached out to. This is Mayfield Phillips in New York, and he's worked in project management for 25 years and has also been out of work for the last few months. And he's found the whole process of applying for jobs with AI to be infuriating. You're interacting with these applicant tracking systems, right?

I just encountered one earlier that it took my resume at the very top of the process, populated my name. That's all. Then there's all of this list of experience it wants me to fill in. I just closed the application. To me, it's disrespectful of your talent to put all of that burden. You ask for a document that has all the information. Use it.

Don't expect us to regurgitate it to you. That's kind of an indicator of what professional life would be there.

Here's Tyler Jensen. He's a filmmaker and editor also in New York. And for years, he's been able to make ends meet through freelance gigs. But COVID really changed all that. Now he's looking for more regular work. And he had one recent AI interview with a bot called Robin, which he found very disconcerting. There'd be long pauses and I'm like, oh, I probably should say more stuff, but...

I don't understand how this is being used against me, for me, if it's truly AI and they're just going to take my responses and like,

transcribe them to text. Doesn't really matter how awkward my interactions are. And yet I don't have eye contact on another person to let them know that this is weird for me. So like, I don't know how my, I don't know. It's one of those strange things that I hope not to get better at. Here's another one. This is Shree from Loganville, Georgia. She left us this message about how she's been using AI to help with job screening.

I have used AI to help me target companies in my area to apply to. I've used AI to write my cover letters. I believe cover letters to be outdated, but some companies still require them. So AI looks at my resume and crafts a very well-written professional cover letter. And then once I've been able to secure the interview, I've

I've used AI to prep to compare my resume to the job description to determine what types of questions I need to be prepared to answer. So that's Sri from Loganville, Georgia. Professor, your book goes through chapter by chapter in detail about what happens at every level of the job search, right? And I'd love to sort of bring some of the richness of that detail and those stories into the show here. So let's start with just when you submit a resume to a place like LinkedIn. Like specifically LinkedIn.

What happens? We see two technologies, and I think Mayfield sort of had an interesting experience there as well, right? That they were asked like, hey, we put in this, you know, your name and your address. Can you add all of the other information in the fields in our spreadsheet? And I think that's actually happening a lot. That when I talked to the CEO of ZipRecruiter, he said that they checked all of their resume parsers online.

on the market a few years ago and they found that about 50 60 of the time the fields like your work experience get copied into the wrong field of the spreadsheet so a lot of information can get lost it's like so basic the problems here so we often i i often uh recommend to job seekers to use

AI tools like JobScan and others that actually help you sort of, you upload the job description, you upload your resume, and it tells you, oh, like most AI tools will think that you have like 80%, 90% or so overlap with the job. And that's what you want to push for. Yeah, hang on here for just a second. Yes. JobScan. Yes? Yes.

Yes. Okay. And there are a couple other tools like that that can help you. I'm not endorsing it, but I know. But I'm saying that like it is helpful because I've had people who have been really frustrated applying. They couldn't figure it out why their resume doesn't come through. They use Jobskin and they, you know, it helps you sort of find the right keywords to put on your resume and sort of think about transferable skills and that ups

your percentage, and then you can get through easier. So we have to really rethink the way we craft our resumes. Generative AI can help, but also just sort of thinking through how can I make it more machine-readable, all of those things. The reason why I wanted to hear you say the word job scan, again, is that I know that in listening to this hour, a lot of folks are going to be taking notes on how to help themselves in their job search. So again, not endorsing job scan, but that's a tool that you...

Actually, I'm writing it down, too, just in case I need it in the future that you suggested that people use. Okay. So, again, like this resume screening, though, what happens? I guess it depends on the company, but for some of these big firms, what do they do with that data? There's two ways. And one is where you have AI look at the job description and just check how much overlap is there to all the resumes.

that is actually not very efficient because job seekers and generative AI or whatever they use, they're pretty genius. So you know that the skills in the job description, you probably have to have them on the resume. So it actually doesn't screen out a lot of people. So it's very inefficient. So what many companies do, they take people who are currently in the job, sort of

If you are looking to hire an accountant and you already have 50 accountants working for you, you assume like, you know, the 50 accountants that work for me, they're successful. They made it through all of the trials and tribulations of hiring. I'm going to use their resumes, feed them into the AI tool and tell it, figure out what these 50 accountants have in common and hire those people, right? Like put the people, the resumes that I feed into you, tell me which one are similar. And that's where...

Actually, when I talk to employment lawyers and other folks who get called in when like AI vendors pitch their software to companies or companies use the software and they feel like, I don't know if this is working, they bring in outside counsel and they found that sometimes or actually more than sometimes

some of these tools predict on kind of weird things. For example, one time in one of the resumes screeners, it had learned that the name Thomas was predictive of success at the company. So if you have the word Thomas on your resume, you got more points. And it probably points to

probably the AI, it's going to sound weird, did what it does best, right? It got a pile of resumes. It did a statistical analysis looking for patterns. And maybe this was for like a tech arm or for a company where you often have more men working than women. And maybe there were more Thomases on the pile. And so that became a predictor. But obviously, it's not meaningful, right? It's just like, it's just arbitrary. It's not meaningful for your... Can I just jump in here? ...pointing us to your capabilities. Yeah, of course. Because this gets very creative.

Because even just at the level of resume screening, I mean, in your book, you write about how preferences are given to those patterns, but they're also downgrading other words, right, that might appear in the resume. Yes, totally.

You have the example of like, I belong to a women's chess club or women's soccer team. And we don't know anything about it. So that could be gender discrimination, right? Like, because probably more women have those words on their resumes, you know, and I think that's sort of where some of the problems with these tools come in, because the scale is just unprecedented, right? If you use an AI tool for all incoming resumes to a company, you could discriminate against hundreds of thousands of

people versus like one hiring manager. I also found out that in one case, if you had the word baseball on your resume, you got more points. If you had the word softball on there, you got fewer points. Again, pointing to gender discrimination, another tool used the word Syria and Canada as predictors of success. That could be discrimination based on national origin, right? Because you're favoring those and disfavoring others. And, you know, there can be all kinds of bias creeping in. And I think what's interesting here is like the companies themselves didn't find this out.

They found this out when they brought in counsel from the outside. So this isn't routine that this comes up, but it's actually routine built into these tools. And we're not doing a good job supervising these tools. Well, let's hear from Camille. She's in Santa Barbara, California. She reached out to us with this story. She works in biotech. She's been out of work since February. And she says the AI-powered applicant tracking systems, or ATS,

ATSs that she's encountered are very frustrating and to your point, professor, she believes they're discriminatory. Not surprisingly, ATS software is designed to weed out as many applicants as possible since employers and hirers are flooded with applications and they can't possibly review them all. So because of this, they find some arbitrary reasons to weed you out. For example, not only might you use too few keywords, but too many and you will be filtered out. Worse, they're almost certainly guilty of illegal discrimination. For example, instead of age discrimination, the system will instead deem you overqualified.

I have a PhD in biochemistry, but I'm also 49. I'm certain that ageism is a factor. So now I'm playing hide and seek with dates as I've been coached to do and avoid the overqualified filter. You can also get blacklisted because ATSs between companies communicate with each other. So say I got rejected from Amgen in March. Now BioRad won't even look at an application from me until September or even later.

Professor Shellman, I keep thinking about this again from the job seekers point of view and the employers point of view, because it actually makes sense to me that a company would say, these are the types of folks that we found are successful in this job. And so we are looking for people with these attributes.

Totally makes sense. But at the same time, doesn't it reduce the chances that they're going to find people that may have some additional skills that they didn't actually think that were immediately relevant to the job? But if a human were looking through them, they'd say, we're working on somehow trying to reach out to certain groups with this product, etc. Maybe we want someone that has connections.

connections with that group. Exactly. And we know that more diverse teams actually are better for business. So hiring the same people that you already have, that you often got into the job with human bias attached, right? Like there was a reason why women and people with disabilities have

underrepresented in sort of the higher echelon of the workforce. And that's partly due to human bias, right? So we have like, you know, you don't want to hire the same people again and again. And you're staying with the skills that you currently have and capabilities where you maybe look towards the future and think like, you know, what I really need is, you know, people who are go-getters, who are creative. And you might not have that in the job, right? And we see this again and again, that if you hire, a lot of hiring managers just pull out the old job description and just add three more skills.

And so you have these like long laundry list of like things where you really feel like, is that really what we need? And I think the problem also by looking at people who are in the job today is like you really should only be looking at skills and capabilities. But we feed into these systems like the whole resumes, you know, a company, one of the largest vendors used to use emotion expression analysis on people's videos. We still see that sometimes pop up.

where I'm like, that has nothing to do with the job. And you just, you might be discriminating against people who smile less or like have different facial expression. Like, don't do that. You veer into territory. We really should only be hiring, looking at skills and capabilities, not the way you look, not the way you talk.

those things should not be part of the hiring decision-making, but AI is bringing them back. So this gets us straight to the interviews and use of AI in interviews. This is Aaron. He called us from Walnut Creek, California, and he's encountered AI bots in interviews. I am six months into a job search, trying to transition careers at a particularly bad time to do it. And I've had...

the interviews with AI bots asking me questions. Over the phone, I've had weird avatars asking me questions in an interview as well. They ask good questions. You're certainly talking to something that can think and carry on a conversation with you, but it is uncanny, and I am still unemployed. Professor Shellman, we have to talk more about interviews here, because right off the bat in your book, you tell this

very compelling story about technology that you saw in 2018, which is like a zillion years ago in AI. Can you please tell us the story? Yeah, I first encountered this technology actually totally at random. I was at a conference in D.C. I needed to get a ride to the train station to come back to New York. And I called the ride share, called the Lyft, went in the backseat and I asked the driver how he's been doing. And he's like, I'm having a weird day.

I was like, "Oh, really?" No one has ever said that to me. And he's like, "You know, I had a job interview with a robot." This was in late 2017. I've never heard of job robots.

And he had applied for a baggage handler position and he had gotten a phone call, but he thought he was talking to a robot. So sort of history repeats itself within eight years. Now we're back to like robot hiring. And, you know, I started digging and I went to a conference and then one of the companies was like sort of showing, demoing their tool. And you would see, you know, sort of how like these boxes would look at the facial expressions and would like write out brow furring, this and this, and, you know, what your emotions were and,

No, I'm going to jump in here. I was just really like thrown away. The detail is incredible, right? Because again, this is 2018. And as you write, on the image of the person who's being interviewed, it's almost like how they make movies, right? There's like little dots, as you said, all over the face, on the eyes, nose and breath. It's exactly like you think it is in a movie. And it was blown me away. I thought it was like, you know, there's new things, like this is a new way of hiring. And when I started looking into it and talking to experts, I was like, oh, wait a second. They were like,

facial expression analysis, we don't know what facial expressions you need to have in a job interview to be successful in the job. There's no science, you know. It's like, oh no. So if you raised your brow too much and got a big score on that, that would equate to, because it gave some, it's like, did emotion analysis, right? Like, disgust, joy. What it would predict upon and was actually calibrated by people who did the job interview before and are now in the job. So if,

You know, if I ask you, what are your strengths and weaknesses? And you smile. And if I smile in the job interview, then I would presumably get a leg up. And, you know, we all know that's totally arbitrary. It has nothing to do with the job, how good you'll be at the job. And there's all sorts of cultural elements. I mean, when it comes to expressions and body actions, there's like...

Men and women are different. The same action can mean completely different things. Exactly. That's why, you know, I mean, I am glad to say that, you know, like one of the biggest vendors in the space here, they have abandoned that technology. It does feel a little bit like a whack-a-mole because then, you know, two years later, another small startup comes up with this idea because they're like, oh, we found this. Like, you can do this. And they have a problem with hire.

So we thought we use emotion expression analysis. I was like, no, we already disproved it. Please stop. But I hope that companies, you know, we are much more critical towards AI than eight years ago when I started this research. And I think that is because we have learned there's bias, there's problems here. We can't just, you know, take these systems and not supervise them. Yeah. Well, Professor Shulman, hang on here just for a second, because we have to take a quick break. And

We'll keep investigating along with you how AI is changing the job hiring and job search experience. This is On Point. Support for AI coverage in On Point comes from MathWorks, creator of MATLAB and Simulink software for technical computing and model-based design. MathWorks, accelerating the pace of discovery in engineering and science. Learn more at mathworks.com.

and from Olin College of Engineering, committed to introducing students to the ethical implications of artificial intelligence in engineering through classes like AI and Society, olin.edu.

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I just want to give you a heads up on a show we're working on for next week. And it comes off the news that the school district of Palo Alto, California, home of Stanford, California, that school district there is actually eliminating in its high schools some of its honors track courses out of concerns of equity and this thought that kids may actually learn better if kids of different levels are in the same college.

So if you are in a district that's also been doing this de-leveling, as it's called, or de-tracking, we want to hear what you think, whether you're an educator, a parent, or a student.

Does it actually increase the quality of learning? Does it make teaching easier? Or does it decrease what you as an educator, for example, can provide to your students of different needs? And for parents and students, does it make learning less difficult?

enriching for you. So we're going to talk about de-leveling or de-tracking. Grab your phone, look for the On Point VoxPop app and send us your story that way or call us at 617-353-0683. That's for next week. All right. Today, again, we're talking about AI and job hunting. And Professor Shellman, hang on for another second with me because we wanted to get the perspective of an employer who

And so we spoke with Claudia Kloss. She's co-founder of the startup cybersecurity company Vidoc Security Lab. And granted, she's a smaller company, not like some of the big corporations we've been talking about. But Claudia recently had two applicants that were in fact actually AI. They weren't human. They were fake. But they got pretty far in the company's hiring process. And that is until Claudia finally sensed that something wasn't quite right. Yeah.

This person was invited to the final stage of the interview with me and then I noticed something was very off. Because it was not only about the bad connection, this person's image was not... He was speaking something and there was a delay between the image and the avatar and his voice.

And also I started asking questions about his previous employees and his jobs. I asked if he was working remotely. He didn't know where the headquarters of the company he said he was working for. He also said that he lived in one of the European countries for a while and he wasn't able to even say a word in the language.

After discovering that this so-called person wasn't really a person, but instead some kind of deep fake or avatar, Claudia says her company has had to re-examine the way they hire people. So we changed totally after this accident. We changed the way we do the interviews with people. So first we take a much closer look to their LinkedIn profile and CV. We invite people to on-site interviews at the end of the hiring process.

And we try to ask culturally relevant questions in the very early stage of the hiring process. So questions like, what was your favorite cafe at the university? To make sure that this person really knows what they're talking about. It's extremely hard to do it remotely because you have access to chat GPT now. We can Google things really quickly, but it's the best we can do.

Claudia's company has released an instructional e-book so that other companies can learn from their mistakes. She understands why large corporations might use AI as an initial screening tool, but they don't use it for her company because it just doesn't make sense for them. She's wary, though, and says there really is no replacement, at least not with the technology as it is. There's no replacement for humans.

You can do the initial screening, but to be honest, there is on the technical level, we don't have a tool that will give you 100% accuracy. You still need humans to double check after AI. And also, unfortunately, because you're using automated process, you can hurt humans in a way because they might be a real human who submits the CV, but it isn't in a format that AI knows.

And, you know, you get rejected by the machine, basically. Professor Shellman, this gets us kind of back to the question of does the AI actually help companies find the kind of workers they want? Are they generally happy with it? And

Do they even know how it works? Do the vendors even know how it works or what these things are screening for? Yeah, yeah. I mean, I think that is like where I think we need to do much more because I think what happens a lot that companies use generative AI or deep neural networks. And part of that is that even the people that built the systems don't always know how the system works.

And what we see is like a lot of AI vendors are startups. They have to bring products to market really quickly. So I think, you know, they all come with good intentions, but there isn't a whole lot of testing, red teaming or whatever you call it, bias testing takes a long time that is

being done here. And then the employers that want to use the software, they also don't want to start pilot testing for months. They just want to use something out of the box. They want to save money. They don't want to hire people to then supervise the system. And this is HR, right? It's usually seen as a cost center in companies, right? It doesn't generate money. So any cost saving you can do, you want to buy these tools. So there isn't a whole lot of checking of the tools. So I think that also worries me. And the companies that use the software, I've talked now to so many chief people officers who told me, oh, yeah,

We use that game or we use this tool and we had the same questions as you raised. We found out it isn't actually working for us. So we stopped using it. That's great. But I was like, can you just publicly say that so that the next company learns? But there isn't a whole lot of appetite for that because those companies are often terrified of class action lawsuits. They would come out and say,

"Oh, we used the game," and fewer and fewer women came through. There might be hundreds or thousands or hundreds of thousands of women who might file a class action lawsuit against that company. So we see there's a whole lot of silence around these tools, and I think that's not helping us to build better tools and know which ones work and which don't.

And that's a problem. And now put on top of that, that link in job interviews, you can't even tell if it's a human or if it's an avatar that's speaking to you. You know, you have to bring in some of these old school things that the CEO is bringing back of like doing in-person interviews because you can't tell anymore. So it's like, you know, we have like AI versus AI. We really don't know who is winning here at the end. It seems like companies are complaining that they don't find the right workers. Job seekers complain. It takes hundreds and thousands.

applications often to even get a job interview. So that's not working. Like, it's really fundamentally broken. Well, let me ask you about specifically there's some of the sectors that you found that AI was being used in the job search. You said trucking is a big one and in the airlines as well in certain places? Yeah. So we see it most often used in like what the industry calls like

high-volume, high-turnover jobs. So we see it often in retail, in fast food. We also see it in trucking that AIs use a lot. Chatbots are used to just figure out, like, okay, do you have a trucking license? Yes, no. And then, you know, you go to the next round. Delta Airlines at one point used AI video interviews to hire flight attendants,

We've also seen at Atlanta Public School use AI as part of their hiring process at one point to hire teachers. So we see it sort of creeping up. We also see it a lot if you want to work in investment banking or banking. Like you cannot really, especially in New York, get past any hiring. It's all AI screens. And, you know, I've talked to folks who were in graduate programs in New York who are like amazing qualified people.

computer programmers, quants even, and they say like, you know, I had like 400 interviews this semester. I can't even get an internship because it's all full of AI screens. Yeah. You know, it suddenly occurred to me that, again, this is always a problem when it comes to data analysis. Like what we might end up creating is a system in which

When workers get better and better at gaming the AI system, like finding what keywords to use or getting AI to help them mold their cover letters, etc.,

What the tool ends up actually selecting for is people who are good at that, right? And not necessarily good at the job that they're trying to be hired for. Do you see what I mean? Yeah, totally. And I think, you know, you find people who are good at gaming the system or figuring out the system to their advantage, right? That's always been the case in hiring.

but it feels like it's now in steroids. And I do worry for people who don't have those skills, right? Like somebody previously said about, like they're worried about age discrimination. That comes to mind. We don't really have any data. We don't know. We know of one case where the Equal Employment Opportunity Commission took action against a company because it did flat out discriminate. If you had your age on your resume and you were over 55, as a woman, you were thrown out and the other time over 60. But we don't know what's happening inside of these systems. So I do worry about

a lot about that. And I think that's not fair to people, right? And also, like, with all of the shows, that's why I said, like, you know, we need to fight back because this is the first generation of AI tools that's coming out. Or, you know, maybe we're in the second generation, but it's pretty early on. And I think what we have done is, like, we automated back

Like, you know, resumes are not very predictive of how successful you'd be in the job, right? And we know that people are very good at looking at the job description, putting the skills that are necessary into the resume. You know, maybe if a two-week Python class and you put in that you're Python and somebody is a master developer, you can't tell those differences.

So like resumes are bad. The same is like with the job interviews. Actually, they're not very good either because some people are very confident and they sort of project competence for that. And then they start the job. You're like, wow, they're really not good at this. They're just good talking about it.

So we use these flawed systems. So I actually think we need to think about how can we hire more holistically? What other can we actually do assessments that the highest prediction is actually, I mean, it's been no surprise to anyone, is for somebody to do the job, to assess, are they going to be good at the job? That's usually not doable for most companies. You're not going to hire 100 people for three months and then lay off 99. But can we build maybe virtual reality assessments or something that looks at the core

core competencies of the job and test for that. But it's really, I mean, it's really hard to do that. It's really hard to test for soft skills, almost impossible, right? If you could crack that, it's a billion dollar assessment industry, you would hit the jackpot. So, but we need to, you know, hire better people

Some cynics, employment lawyers told me, you know, what would be more fair? Just run a random number generator. That's actually more fair. Everyone has the same chance to be chosen. And it's as good as any of these tools. That does not bode well. Probably most companies don't want to do that, no.

Can you hold up for a second, Professor? Because I just want to, before we run out of time, I just want to get the voices of our listeners in here once again, because they really supplied us with so many actual first-person experiences. Here's Kyler from Independence, Oregon. I found that when I am making a resume or a cover letter,

It's almost as bad enough that I have to copy and paste from the job description itself or I'm not even considered for even an interview in some cases. All right. And here's Jason from Salinas, California. He actually serves on multiple hiring committees for his college employer. And he's noticed recently, as we've been talking about, an upward trend of applications that are crafted with AI. But Jason says they're all pretty bad.

It sticks out like a sore thumb. It's obvious that it was AI generated to the point that it is almost like students of mine copy and pasting and getting caught in terms of doing plagiarism and it gets candidates mooted.

So it's a word to the wise that if you're going to use AI to try to get a job, use it in such a way to where you mask that it's AI because it's still not to the point where you can just give it a prop, ask it, and it's 100% going to come across as genuine, particularly in the job application process.

So Professor Shulman, I have to say, I want to continue to remind myself of the advantages that AI can bring. We talked about the volume problem.

And ideally, one of the promises of AI is that if care is taken, it should reduce the biases that can come with human-driven processes, right? Because no one's ever said that a face-to-face interview between two people is free of bias, right? We have our own biases there. Totally, totally, yes. But at the same time, we're still, maybe in a couple of more generations, we're

We'll get there. But what you were saying a little bit earlier just made me think that ultimately the problem isn't AI. The problem isn't people. The problem is just like how hard it is to find the right people. It's the hiring process in general. Exactly. You solve that, though, and you have a quadrillion dollar product.

Totally, totally. And I think it just, you know, like the move to AI or to digitization just has shown us how flawed the system really was always. And we know that from the data, right? We know that, like, I think within a year and a half, almost 50% of people quit. You know, a lot of people complain, that's not the right hire, yada, yada, yada. It is just incredible hard work.

to hire the right people. And often it's like, you know, when they start the job and they're in the job, you sort of finally get to assess how good they are at the job, right? It is incredibly difficult. And also, like, there's so many untrained folks, right? Like, I mean, you know, lots of people have to hire. They've never been trained for it. So it's really difficult. Yeah, you know, and my conclusion to that is maybe that's the way it should be, right? Maybe some processes shouldn't be easy and for some processes, this, like...

or this Shangri-La of a frictionless process just simply will never happen. We're talking about people at the end of the day. Maybe that's how it should be. But we have only about a minute left, Professor Shellman. And once again, I'm thinking about all the people listening who are particularly still looking for jobs in this fraught new environment.

What advice would you give them? Are there specific tools you would recommend? You know, first of all, it's like, you know, I always tell that to every job seeker. It is not you. It is a numbers game, right? It's not that you're not qualified. It's just like, and everyone knows that. Like I talked to the big job seekers.

They know that it takes hundreds, thousands of applications for anyone to find a job. So it's not you just keep doing it, chugging along. Look at all the stuff that we see out there, like how to produce a machine-readable document

So I think, you know, like old school, we would tell people, like, try to stand out. That's like a human thing to say, like have two columns, colors, images, none of that. Machines can't read that. Have short quantifiable sentences, all of that. I have like, you know, and find me on LinkedIn. I have a lot of tips on there that I think works for most of the systems, right? I don't actually know which system works every time, but we know a little bit how things work. So don't give up.

And we will have to change the system eventually.

Well, if you're looking for Professor Shellman on LinkedIn, it's Hilke Shellman, S-C-H-E-L-L-M-A-N. You invited that professor. Yes. And there's only one of me. You will find me. And her book is The Algorithm, How AI Decides Who Gets Hired, Monitored, Promoted and Fired, and Why We Need to Fight Back. Professor Shellman, thank you so much. Thank you for having me. I'm Meghna Chakrabarty. Still not a bot. This is On Point. On Point.

Support for this podcast comes from Is Business Broken? A podcast from BU Questrom School of Business. How should companies balance short-term pressures with long-term interests? In the relentless pursuit of profits in the present, are we sacrificing the future? These are questions posed at a recent panel hosted by BU Questrom School of Business. The full conversation is available on the Is Business Broken podcast. Listen on for a preview.

Just in your mind, what is short-termism? If there's a picture in the dictionary, what's the picture? I'll start with one ugly one. When I was still doing activism as global head of activism and defense, so banker defending corporations, I worked with Toshiba in Japan. And those guys had five different activists, each one of which had a very different idea of what they should do right now, like short-term.

very different perspectives. And unfortunately, under pressure from the shareholders, the company had to go through two different rounds of breaking itself up, selling itself and going for shareholder votes. I mean, that company was effectively broken because the leadership had to yield under the pressure of shareholders who couldn't even agree on

on what's needed in the short term. So to me, that is when this behavioral problem, you're under pressure and you can't think long term, becomes a real, real disaster. Tony, you didn't have a board like that. I mean, the obvious ones, I mean, you look at, there's quarterly earnings, we all know that. You have businesses that

will do everything they can to make a quarterly earning, right? And then we'll get into analysts and what causes that. I'm not even gonna go there. But there's also, there's a lot of pressure on businesses to, if you've got a portfolio of businesses, sell off an element of that portfolio. And as a manager, you say, wait, this is a really good business. Might be down this year, might be, but it's a great business.

Another one is R&D spending. You know, you can cut your R&D spend if you want to, and you can make your numbers for a year or two, but we all know where that's going to lead a company. And you can see those decisions every day, and you can see businesses that don't make that sacrifice. And I think in the long term, they win.

Andy, I'm going to turn to you. Maybe you want to give an example of people complaining about short-termism that you think isn't. I don't really believe it exists. I mean, you know, again, I don't really even understand what it is. But what I hear is we take some stories and then we impose on them this idea that had they behaved differently, thought about the long term, they would have behaved differently. That's not really science.

Find the full episode by searching for Is Business Broken wherever you get your podcasts and learn more about the Mehrotra Institute for Business, Markets and Society at ibms.bu.edu.