Speaking of Psychology - Future of Work (SOP90)

Episode Date: September 25, 2019

From automation, to artificial intelligence to employee surveillance, technology is rapidly changing the way we work. It’s raising ethical questions, concerns about the future of the job market and blurring the lines between the personal and professional. Tara Behrend, PhD, associate professor of industrial-organizational psychology and director of the Workplaces and Virtual Environments lab at The George Washington University, explains what the future of work will look like. Join us online August 6-8 for APA 2020 Virtual. Learn more about your ad choices. Visit megaphone.fm/adchoices

Welcome to Speaking of Psychology, a bi-weekly podcast from the American Psychological Association. I'm your host, Caitlin Luna. From automation to artificial intelligence to employee surveillance, technology is rapidly changing the way we work. It's raising ethical questions, concerns about the future of the job market, and blurring the lines between the personal and professional. What does the future of work look like? Our guest for this episode is Dr. Tara Barron, Associate Professor of Industrial Organizance, Psychology and Director of the Workplaces and Virtual Environments Lab at the George Washington University. Dr. Barron is also on the steering committee for APA's Technology, Mind, and Society Conference planned for this fall.

Welcome, Dr. Barron. Thank you. I'm happy to be here. That's great to have you on our show. Many people may not know what their employers are tracking about them. What are some of the typical methods of electronic surveillance? Sure. Well, you raised an interesting question because one of the challenges about electronic surveillance is that you might not know what's being tracked about you.

And I'm certainly not suggesting that everything about your behavior is being tracked, but certainly the technological potential is there. So this could be something as straightforward as reading your emails or using biometric sensors to tell where you are in the building and who you're talking to. Or we could imagine even more invasive technologies like tracking your facial expressions or your tone of voice to determine your emotional state and maybe even intervening if it sounds like you are getting frustrated. So what are some of the reasons why employers track all this information? I understand there are some obvious reasons, but reading email seems very like a time, much really time-consuming thing to do. Sure. Well, we can think of useful and beneficial reasons to monitor people or potentially less useful or less beneficial reasons. Some of the reasons an organization might want to monitor their employees might be to give them feedback. to help them improve their performance, or to alert them to a potential safety issue,

or to help them become more efficient and productive. These are all fairly reasonable, in my opinion. We can also imagine, though, using the technology simply because it exists. And this would represent an extreme form of micromanagement that I would certainly advise against. So how was it evolved over the years? Well, in the early years, electronic surveillance,

looked like maybe using software to track keystrokes to determine who is being efficient in their work or a video camera, a video surveillance camera installed in the office to determine people's comings and goings or maybe to detect theft. Some of those systems weren't necessarily focused on the employee intentionally. Maybe they were a security camera to detect shoplifting, but they ended up catching people's behavior incidentally or accidentally. that's advanced very quickly to the point where now we're using those same technologies to monitor employees intentionally. What does your research tell you about how this data is being used?

You mentioned it might be used for to help employees improve on the job or to see who's being efficient at their jobs. But how do we know how do we know how it's being used and what are some of the risks of storing this data? Sure. We asked some great questions there. Let me give you an example of an Uber driver. So if I'm an Uber driver, I have my phone on me.

at all times. And that phone has an accelerometer in it. I can tell how fast I'm going and how quickly I'm breaking. And it has a microphone and a camera in it. So Uber can use that phone to track whether a driver is driving safely. And if they're not driving safely, they can do a few things. They can intervene with a warning message that says you need to drive more carefully. It can just kick that driver right off the platform if they feel like it because that driver is not protected by any sort of collective bargaining agreement. Or it can make that information public so that customers can choose not to accept a ride from that driver.

So that's an example of the same technology, the accelerometer, being used for either a beneficial developmental purpose or for a very severe authoritarian purpose. It's not the tool itself so much as the way it's being used. As far as storing the data, of course there are risks of data breaches and I don't think I need to go through those because certainly they're in the news every day lately about the ways that our information can expose us to danger.

There's also the risk that information that's collected for one purpose is then analyzed and used for a different purpose later down the road without the employees consent or knowledge. And this represents one of the greatest fears, I think, for employees is that you don't know what's being collected, you don't know how it's being used against you potentially, and you have no recourse,

if a decision is made using that data because there's no information provided to employees. And what are some of the ethical considerations employers need to have about data collection and storage? So when we think about the rules of organizational justice and ethics, transparency is one of the primary principles we want to uphold. Employees should have a voice and decisions that affect them. They should have the opportunity to say that this decision was made on inaccurate data and to refute it or to appeal it or to raise it to a human authority of some sort. And the danger here is that not only is this not happening, but it's impossible.

It's impossible to know the ways that data is being combined to make decisions about people. And so an employee can't refute it because they don't know what basis the decision was made on in the first place. Yes, it seems like it's very complicated. It's very complex topic. And do we behave differently when we're being monitored,

electronically versus when a person's looking over our shoulder? It's a good question. So there are some things in common between being monitored by a human and being monitored by technology. In both cases, there is a social facilitation effect, which means that it is certainly taking away some of our attention. We are aware of the monitoring and we do change our behavior accordingly. Essentially, it focuses our attention on the things that are being monitored. If I'm tracking your step, for example on a Fitbit, then you'll maximize your steps, but maybe you'll take on other unhealthy behaviors that aren't monitored.

All right, so in both cases, we're focusing the person's attention and what's being tracked. The difference when we talk about electronic surveillance, though, is that it's invisible sometimes, and it's also permanent. It's also focused only on you. So if your boss is watching you,

your boss is also watching other people and their attention is not always on you as an individual. But in the case of electronic surveillance, your attention, the attention of the monitor is always focused on you and you have very little control over that. What we know from many years of research is that when people feel like they have no control over a situation, they react very badly. Yeah, and I wanted to touch on that because your team at the Wave Lab conducted a study in 2015 that found that people with more complex jobs who were monitored heavily at work rebelled, if you will. They basically felt violated

and they would not go above and beyond. And you found the opposite to be true for people with less complex jobs. They didn't mind being monitored. What was really interesting about this is when you replicated the study three years later in 2018, you found that both types of workers felt violated by being heavily monitored.

What do you think changed during that three-year period of time? Right. So what that study, I think, is tapping into is a change in people's expectations about what is reasonable in the workplace. And if we think about what happened in the years between 2015 and 18. That was the introduction of GDPR, Cambridge Analytica,

many, many high visibility data breaches, and also many apps and useful services that rely on our data to give us services. So the world changed dramatically in those three years in terms of how people think about data and privacy and the rights of others, whether it's organizations or societies, to track our behavior for their own purposes.

So what I think happened in those three years is that people's expectations changed, their baseline changed, and they were better able to see that this has now become part of the world. It doesn't mean that they necessarily like it any better, but it does mean that their expectations for what's normal have changed. Meaning they felt less comfortable because of all the data breaches that were happening and things they've seen out there in the world. Well, quite the opposite, that a greater percentage of the sample in 2018 said that this is just the way it is now. They had sort of resigned themselves to the fact that this is how the world works. And so what disappeared is the difference between high complexity and low complexity jobs. That low complexity jobs, think of a call center.

When you call customer service and it says this call is being monitored for quality assurance, right? That has always been the case, that people in low complexity jobs did not feel in things. entitled to their privacy for good or for bad, but say a doctor did feel entitled to their privacy and now nobody has that sense of expectation. So even though people feel like, oh, this is just part of the world right now, it doesn't change how violated they might feel as well. Right. So a person's reaction to having their privacy violated depends on a lot of factors. One of the factors is their personality. So some people are very sensitive to any restriction on their autonomy. And And if you tell them they can't do something, it's very offensive to them,

and it's also the only thing they want to do after that. So if you know any three-year-olds, right, they all feel that way. If you tell them they can't have a cookie, they can't think of anything but that cookie. So some adults feel the same sense of really valuing their autonomy and really resisting any attempt to restrict their autonomy. The thing about electronic surveillance is that it represents a threat to someone's autonomy. it is a restriction about the way they can go about doing their work. Again, because if I, if I'm telling you that your work value is being tracked based on how many

minutes you're in the office, because I counted that with my surveillance system, I'm telling you now that I've determined what makes good performance. And I've decided that it's how many minutes you spend in the office. I've taken away your discretion to decide what makes good performance. So what I think you're saying is in some ways it might be more productive to be monitored, because we might focus on the things that are being monitored, but in other ways, it's counterproductive. Right. It depends on what's being monitored.

In all cases, tracking something pulls people's attention to it, and it serves as a motivator and a goal. So if I choose the wrong target, then I will hurt people's performance. This is the danger of allowing the tool to drive decision-making instead of thoughtful strategy about what good performance really looks like. So I'm a college professor. My performance should be judged based on my research portfolio and based on my teaching. And if I were to instead say count students in my courses, just count them, right?

Well, that doesn't tell me anything about the quality of the instruction I've given. Or if I was to look at the online rate my professors, right? That's a few students out of a thousand. and whether it would be a mistake for me to optimize my behavior on what rate my professor said. So it's not about whether it's measured or not measured. It's about choosing the right set of tools and right set of behaviors to measure that really are the most important ones. And despite people feeling like this is part of the world yet feeling violated at the same time, do you think people in general feel more comfortable with being monitored at work?

Like they just know, okay, my employer can access my emails at any time. They may log my keystrokes. They may look at my internet history. What are you seeing in your research? So part of this is what other scholars call the privacy calculus, that people engage in a sort of cost-benefit analysis to determine whether the loss of their privacy is worth the benefit they get back. And the benefit might be in some feedback or some productivity.

gains or in being able to do their work better. So for example, there's a hospital in California where the nurses all wear location badges. And in other circumstances, people might find that very invasive. But in this case, the nurses can use it to quickly locate each other in the case of a crisis. And it makes them much more efficient. And they feel like this helps them do their job better. So if monitoring is implemented in a way that helps people feel like they're doing their job better, they're getting feedback to optimize their performance, then they'll accept it. And this is, you know, the quantified self is a good example of that, right? People love being able to track their calories and their input and their nutrition and their sleep quality because it helps them be healthier.

But if someone was using that to punish them somehow, they would not feel the same way about that exact same tool. Yeah, speaking of that, there was a story in the Washington, to post recently, employee monitoring in terms of health and wellness. And that story they profiled the company with a health insurance provider that offer those digital fitness trackers to employees. And the CEO had access to that data and would basically use it to encourage employees to make healthy decisions. And one of the employees they profiled had recently had a heart attack, I believe. And so the CEO would say, you know, I saw you're getting all your steps in and you're making healthy choices on the job. Great, great job. And that employee didn't feel violated. He felt

encouraged by it that the CEO really cared. But where some people might read that story and feel like, oh, incredibly, like this is crossing so many boundary lines. So what is your take on that? I love that example because it shows us that it's really not about the technology. It's about the organizational culture of, in this case, trust and supportiveness instead of punitiveness. So you could use that same data to fire somebody who is costing you too much in health insurance, right okay i've noticed that you don't get enough exercise and so you're a liability and you're out of here and if employees fear that that's how their data will be used they will not accept the tool but that comes from a climate of positive culture and ethical leadership and communicating clearly to employees about

how their data is being used and what it's being used for and so this is an example of a manager using the technology to show his support and concern for an employee and not to punish him for being expensive, essentially. When I was writing these questions, I thought so often of the Twilight Zone, which I've watched recently. And they have one episode about people being obsolete. And there's another episode about automation, which is really fascinating that this was a big topic of thought and concern in the early 1960s, late 1950s. And here we are. today in 2019, the Brookings Institution issued a report earlier this year that said that up to a quarter of U.S. jobs are at a high risk of automation. And this would affect likely food preparation

and transportation, and this would impact lower wage workers the hardest. What is your take on this report and the direction we're going with automation? So fears of automation have been top of mind for the last hundred years, at least, starting in the 1920s and certainly persisting to today. And I think Herbert Simon writing in the 60s said all the evidence suggests very clearly that economic institutions, not technological institutions, determine unemployment. And I think that's correct. That automation replaces tasks, not people, and jobs change. They become more complicated, but they don't go away. We add jobs to the economy with technological innovation. The evidence is very clear here that there are very few jobs that can be fully automated to take the example of

a food preparation or say a McDonald's cashier there are plenty of McDonald's now that have automated the cashier function and so instead of laying those people off those same employees can bring food to people's tables and provide a higher level of customer service and as the world changes will start valuing that sort of task more right so so I think it's useful to think not in terms of jobs but in terms of tasks. Truck drivers do plenty of things besides drive trucks, right? So they are a security guard for the cargo, they're managing relationships with vendors, they are, they are professionals that drive trucks and also do other things. So if the driving part can be automated, then they can do a better job of those other tasks. Humans are innovative and creative and they are

very capable of finding new ways to contribute to the economy. As we invent new problems, we need new ways to solve those problems. So I'm not, I'm not worried about automation, creating unemployment because it's never been the case, and I don't think it will be the case in the future. That's really interesting because I think a lot of, there's a lot of fear around this topic, because you think, okay, my job's going to go away. The grocery stores are all going to have automated registers. Where are the people going to fit in? Like you said, that's a very fascinating example about the truck drivers doing so much more than just operating the vehicle. Do you think, I mean, so you said, we don't have a reason to be entirely pessimistic that there's certainly

ways to be optimistic about the future and saying that there will still be roles for people in these industries. Right. ATMs didn't eliminate bank tellers. And as personal finance became more complicated, the things that bank tellers needed to do became more complicated, they're better to able to do that now because they aren't having to just make change for people. Right. So if we can automate some aspects of a person's job, we can imagine them working with the automation to improve overall productivity of the economy and not worry about that job going away, but of course it will change.

Jobs will change, and I think they will continue to change very rapidly from now on, which means that we all need to get better at adapting to a changing environment. And instead of imagining that you are training for a particular job that you will continue to do the same way until you retire, I think we all have to think of ourselves as continuous learners, no matter where we are in the economy. For people in my job and college professor, I can't get away with not using PowerPoint, right? I can't get away without having a blackboard site where I post things online.

I have to adapt. And so does everyone else. So that raised a lot of interesting questions about some of the trends we're seeing in this country, about industries that are changing and jobs that are going away. Do you think that what's missing is that retraining and education component? I do. I think we all need to think of ourselves as lifelong learners. And also corporations are going to have a much greater responsibility to be a part of that lifelong learning.

I think we'll see an uptick in corporate universities and partnerships with community colleges to make sure that people are always updating their skills and keeping them current. I always say that I would never go to a doctor who hadn't learned anything new since medical school, right? The expectation is that that doctor is keeping up with new research. That's a very difficult thing to do because the research is generated so quickly. but other professions don't have that institutionalized sense of continuous learning at this current moment in time and I think it will no longer be sustainable for them not too. And what is the role of psychologists in all this? Where do you see psychology fitting into helping move the economy forward as we progress in this century?

So psychologists know a lot about how people learn. We know how to create an environment that is best suited to a person that's the person needs and motivations, and to ensure that that learning transfers into job performance. We know a lot about organizational structures and the things and the barriers that can get in the way of that transfer. We know a lot about how technology can potentially support the learning endeavor, and we also know about the individual factors that might get in the way in terms of low self-advocacy for learning or a background knowledge that isn't sufficient.

there are things we can do throughout a person's lifespan to support them as a learner. In my focus, as an industrial psychologist, I tend to think mostly about adults, but that's becoming not so sustainable anymore. We need to now start thinking about people's early experiences that prepare them for the world of work as well. I find it very heartening that you say that you're someone who is so steeped in this field that you're not concerned about automation and AI taking over the world. I'm sure that's a common fear you hear from people.

Well, of course. And I don't mean to sound overly glib about the issue, right? Of course, the economy will change dramatically. And anyone that isn't ready to adapt to the new environment will feel very uncomfortable with that change. And about the hiring process. You gave a talk at APA a couple months ago at our headquarters, and you talked about how your lab is researching the influence of technology in the hiring process. Can you talk about your research and what trends you're seeing?

Sure. So there's a lot of excitement in the industry in the kind of selection industry right now about ways that AI and algorithms can support the hiring endeavor. And when we think about what's supposed to be happening in the pre-hire interview, for example, while we're supposed to be measuring a person's skills and qualifications and making a decision, yes, no, should we hire this person? And so what an algorithm can do is how, remove some of the biases that interfere with that decision-making process or that measurement

process. In terms of the measurement of skills and competencies, we know that a resume tells you almost nothing useful about a person. But are there better ways to measure what someone can really do on the job? And the answer is, of course, yes. There are vendors now who claim that they can use AI to do a better job. I remain very skeptical until I see the evidence of whether that's true. this is the effort that people are putting in at the moment. And then on the decision-making process, that's about combining various sources of information to make a judgment. And there, we've been using sort of old-fashioned algorithms for a long time, right?

How to combine a person's intelligence with their work experience, with their personality, to make an overall decision, right? That's an algorithm. Now, because of new technologies, we can combine many hundreds of sources of information. instead of just a few. Of course, the danger is that these algorithms can become biased very quickly. So my research is about, one, whether people accept these new technologies, and two, what the potential dangers are,

what are some of the sort of extraneous factors that might sneak in there that we can remove to make sure that people have a fair chance to demonstrate what they can really do. And in the talk you mentioned about people recording videos of themselves during the interview process and that that being analyzed not by humans but by an algorithm. And so what does that, how does that influence the hiring process? So there are a few companies that are selling technology that analyzes people's facial expressions, tone of voice, and word choice during the interview process to look for secret patterns

and try to make a decision. they are still in their emphasis. But the idea would be that let's say I notice in my current best employees, they all look up into the left when they answer questions or they all have a little smirk. I could use this technology to measure that same characteristic in some applicants and see who looks up into the left when they answer a question or who has a smirk. Now, the obvious reason not to do that is that there's no good reason to think that looking up and to the left has anything to do with job performance.

And what we know is that we absolutely must focus our energy on skills that are related to job performance and not these cute little tricks of technology. Right. We don't want to just say, oh, look, well, this person smiled 15 times and that means they will definitely be a good performer. So there are ways to do this that are purely technology driven like that. Then there are ways to do that that are more skills driven. And that's obviously the way that I would advocate.

Obviously you need that human component during the job interview process to assess how does a person, how are they, how are they, what are they like in person? How are they responding to my questions? What's their demeanor like? That technology just simply cannot do because they're not a person. Maybe. I mean, the danger of introducing the human element. limit to decision making is that sometimes humans are biased. And not intentionally, but we all have a sort of default towards liking people that are similar to us and liking people that make us feel

comfortable. And that may or may not translate to good job performance. So I might like people that went to my same alma mater. There's no reason to think that that will translate into being a better salesperson. On the other hand, someone that puts me at ease and makes me feel comfortable, that very well might translate to being a better salesperson. So, So the question is always, what job-related competencies am I really tapping into here? Or is it just my personal liking for them based on who I am? Because that is a way to potentially make decisions that are unfair. I guess I hadn't thought about that so much.

I was thinking more just about you might need a human in the picture to not to assess how a person is. But I hadn't thought about, okay, if a person, maybe they don't put you totally at ease, but maybe they'll do a great job at X or Y job. You know. Right. The core principle in trying to predict job performance is to first figure out what are the skills and competencies that I think will lead to good job performance. And certainly being a relatable human could count as one of those things. It's not the only one, but it's certainly one of them. I don't want that person who's really antisocial and doesn't form good relationships with their colleagues. That's not a good employee. So that's the first question. What are the skills

and competencies that relate to good job performance first. The second question is, what is the very best way to measure them before I hire a person? And the very best way to measure them is probably not looking at their resume. It's probably not analyzing their face with an AI algorithm, right? It's probably one of the methods that we've known works for a long time, like a highly structured human delivered interview or a structured assessment that is built by qualified psychologists. So we know that all humans have bias. But humans create those algorithms that do assess people.

How do you ensure that there's no bias in algorithms created to assess people, say, for the hiring process? Sure. So to answer that, let me first clarify what we mean by an algorithm, because I think people hear the word algorithm and they imagine a black box with many thousands of variables being combined in some secret way. But that's a specialized case. So an algorithm is simply a set of rules for how to combine information. And if we take the example of a job candidate, let's say I know three things about them. I know their verbal ability. I know their math ability.

And I know how conscientious they are. So I have three pieces of information. I could create an algorithm that says their overall score will be 40% math, 30% verbal, 30% conscientiousness. That's an algorithm. Another algorithm would be to say they're all weighted equally. Another algorithm would be to say, I don't think constant. to injustice matters very much, so let me ignore it, and I'll just take the first two pieces of information.

Those are all algorithms. There are rules for combining information. And in those cases, I came up with them off the top of my head, so they are strictly based on my idea of what I think leads to good job performance. Well, what do I know, right? I'm not an expert. I'm making a guess. So another way to make an algorithm is instead of deciding off the top of my head how to combine

this information, I look to the data itself, and I try to, try to figure out the equation that leads to the best prediction of performance. And to do that, I need a set of people who have already demonstrated good performance. So I'd go to all of my employees, and I would measure those same three things about them. And I would see what are the patterns that suggest good performance? And I might discover, oh, well, conscientiousness is very, very important. It turns out to predict performance very well. So let me give that a higher weighting, just based on the numbers, right?

That's not my judgment anymore. and verbal ability turns out to not be as important, but math ability is very high. So now I have an algorithm that's not based on my opinion, but it's based on the patterns of correlations in the data itself. Now, that doesn't mean I've removed bias. It means that there are different kinds of biases that exist there. In the first case, from using my judgment, the biases are based on my background and who I am and what my opinions are.

In the second case, the bias comes from the real world. So what if my organization really is very sexist and women struggle to do well? And I've basically canonized that sexism. I've put it into the rules for how new people will be hired and I've ensured that I will continue to be sexist, essentially. So a data-driven algorithm will do its best to replicate the real world. If the real world is biased and unfair, then the algorithm will be biased and unfair. too. I'm repeating. So there are lots of news stories where this has happened where say it was discovered that belonging to a women's organization led you to get less likely to be promoted, right?

Well, we don't want to now incorporate that into our decision making for new people we hire and we say, oh, you're a member of a woman's organization, so we probably shouldn't hire you. That's obviously sexist. But that didn't come from me personally being a sexist. It came from the data It came from the fact that my organization had bad practices and I've taken those same habits and put them into the rules for new decisions. What is the future of electronic surveillance in the workplace? Gosh, I wish I knew. I think there are lots of people getting excited about facial recognition technology. There are tools being developed all the time to try to make inferences about people's emotional states and their competence based on the way that they speak and what their face is looking like.

A lot of these tools were initially developed for military and national security applications that are now being introduced into the workplace. And that does seem to be the way that things go. That tools are developed for governments and then organizations adopt them and use them in ways they were never intended to be used. I sincerely hope, and I think I'm correct in that we will soon see more regulation around these technologies, that it's currently the Wild West and organizations can do almost anything they want. But that will no longer be tenable in the future as we realize just how invasive these tools can be and how dangerous they can be. Do you have an example of one of those talking about facial recognition technologies and how it might be misused or what you've been seeing?

Well, I think facial recognition in the job interview is a good example of how it could be misused, right? We have very good tools for detecting emotions in faces. And, And just because we can doesn't mean we're learning anything about their potential suitability for that job in the future, but we're still using that information. Or you could imagine using it to screen people in their offices and someone who looks disgruntled grabbing them. I don't have any examples of that happening, but I can, I think it's not that much of an imaginative stretch.

Yeah. So it's really fascinating. We're talking about all this technology that's developed for a very specific purpose, but then being reused by organizations in ways that it wasn't intended. So do you think like your philosophy about AI and automation that this will be, you know, the human elements will prevail overall? Is this as we move forward into the future on things like facial recognition? I think so.

I think we're already seeing a more advanced conversation about the dangers of these issues in Europe than we are in the U.S. And again, I think we have been in a period of rapid growth and excitement that is now facing a more sober reality. That some of these tools need to be managed more carefully. They need to be regulated more carefully because people's livelihoods and their health are at risk, if not. What are some of the other topics you're researching at the Wave Lab? So I'm very interested in how we can support people to enter the new world of work. What does it mean to be workforce ready when things?

things keep changing. And I mentioned earlier that I think part of that means stronger partnerships between corporations and educational institutions. Some of the things I'm working on right now have to do with what those partnerships look like, how we support them best, how technology can support new kinds of learners in new kinds of workplaces. Yeah, that's really exciting. Like you said, things change so much that the jobs students are going in for right now and they're in their majors. work at a university, you know, are those going to be around in 20, or what will they look like in 20 years? Like you said, they are constantly evolving and that sort of thing. Right. Well, we, they will definitely not look the way they look right now. So what we need to do is train people

for a set of core skills that can carry through any sort of job changes. Things like communication and collaboration and stress management and leadership. These are things that are not trivial. They used to be called soft skills. No one calls them soft skills anymore. because we're realizing that they're essential. And working with automation, understanding probability, understanding statistical concepts, these are things that will always be needed and will probably become needed much, much more

as we're able to automate things like non-routine manual labor. So the things that we said, you said termed soft skills, which is actually the word that came to my mind, so I'm using the outdated term, those are things that an AI, or we can't train a robot to do. do, if you will. It's just where you need the human element. You need emotional intelligence in the workplace. And those can translate to a variety of careers. I think just to somewhat what you're

saying, that these skills, like you can learn certain skills on the job or in school or through additional continuing education, but these personality, personal skills, the old soft skills, if you will, really will help people move into the future and sustain them as in the changing workforce. I think it's amazing what AI can do. And certainly robots can provide elder care. They can deliver lectures. One of my favorite experiments was done on a large online class. And one of the TAs was a bot and nobody knew and they loved her. They thought she was great. So AI can do a lot of things that maybe seem like interpersonal sorts of tasks. But, you know, a robot can't love you, Can't inspire you, can't persuade you, can't negotiate with you.

And those things are essential in the workplace. Thank you so much for joining us, Dr. Barron. It's been a really fascinating conversation. Thank you so much for having me. To learn more about the Wave Labs research, you can visit wave-dash lab.org. To learn more about APA's technology mind and society conference held this October, visit tms.ap.org. We want to know what you think about our show.

You can email me your comments and ideas at K. Luna at APA.org, K-L-U-N-A at APA.org. Speaking of Psychology is part of the APA podcast network, which includes other great podcasts such as APA Journal's Dialogue about new psychological research and progress notes about the practice of psychology. You can find all our podcasts on iTunes, Stitcher, or wherever you get your podcast. You can also visit our website, Speaking of Psychology.org to listen to more episodes. I'm Caitlin Luna with the American Psychological Association.