Speaking of Psychology: Social robots and deception, with Jeff Hancock, PhD

How people interact with robots is influenced by the robots’ characteristics. Whether a robot has eyes or arms or a human-like voice affects our response to them. Jeff Hancock, PhD, has studied the research to date on social robots and learned that robots’ perceived warmth and competence have the strongest effect.

Dr. Jeff Hancock is founding director of the Stanford Social Media Lab and a professor in the Department of Communication at Stanford University. Hancock works on understanding psychological and interpersonal processes in social media. His research team specializes in using computational linguistics and experiments to understand how the words we use can reveal psychological and social dynamics, such as deception, trust, intimacy and social support. Hancock is well known for his research on how people use deception with technology from sending texts and emails to detecting fake online reviews.

Kim Mills: Hello and welcome to Speaking of Psychology, a podcast produced by the American Psychological Association. I'm your host, Kim Mills. Speaking of Psychology is a podcast for anyone with an interest in the science of psychology. We talk to psychological researchers, practitioners and educators about any and every aspect of psychology and its application to the world around us.

Dr. Jeff Hancock is founding director of the Stanford Social Media Lab and a professor in the Department of Communication at Stanford University. Dr. Hancock works on understanding psychological and interpersonal processes in social media. His research team specializes in using computational linguistics and experiments to understand how the words we use can reveal psychological and social dynamics, such as deception, trust, intimacy and social support.

Dr. Hancock is well known for his research on how people use deception with technology from sending texts and emails to detecting fake online reviews. We're fortunate to have him here today with the American Psychological Association.

Welcome, Dr. Hancock.

Jeff Hancock: Thank You, Kim.

Kim Mills: So, I wanted to start by talking a little bit about social robots and your work in that arena. The first question is just to explain for the audience what's a social robot as opposed to any other robot.

Jeff Hancock: Right, right. Yeah, social robot is really broadly defined — basically any robot that's situated with humans. So, a couple of definitions are that they should be socially evocative, sociable so any robot that's designed to essentially work or interact or evoke responses from humans.

Kim Mills: So, it's not a Roomba, for example?

Jeff Hancock: Well, you know it's funny you should ask that. A Roomba is a good question and our group thought a lot about that. Sometimes, a Roomba could be made into a social robot. You put some little things on it and amazingly people will really interact with that robot as if it's, you know, interacting with them.

Kim Mills: But guess that in the corner there.

Jeff Hancock: Get that work done for me.

But no, typically it's, it's robots that are designed to interact with the humans, so it can be in workplaces. So, factories now often have robots and a number of them now have been sort of personalized — made it look a little bit more human so that the workers around them can understand what the robot’s doing, what its intentions are, where its attention is.

Kim Mills: Mm-hmm. So, talk a little bit about the research that you did. I understand you looked at like, the last decade of all the research that involves social robots. What were you looking for? What did you find?

Jeff Hancock: Right. So, the group I was working with was Byron Reeves and Sunny Leone, both at Stanford with me and we had a group of ten RA’s work on this project where we looked at a decade worth of research on social robots and it was fascinating and a lot of work.

There were almost 7,000 articles that in Google Scholar that referenced social robots and then we narrowed that down to about 1,400 that mentioned social robots but then also had a robot interacting with a human or looking for some sort of social response.

So, there's been you know over a thousand articles on social robots and it's across a dizzying array of disciplines. So, psychologists, computer scientists, engineers, anthropologists, it's, it's pretty amazing.

So, we looked at all of those across a decade and then the thing we got really excited about was we found the photo of every robot that was in that decade's worth of work and found as many photos as we could for each robot and so we sort of had like early census, if you will, of every social robot that had been published about. And it turns out there's 342 that we found over that decade. So, there was sort of like a one of the first collections of all social robots that, that's been put together.

Kim Mills: And what were you trying to find by looking at that?

Jeff Hancock: Well, my colleague Byron Reeves had this insight when we first started the project and, and one of the reasons I got involved. So, I usually study things like social media so how people interact through technology. But, I remember having a great a meeting with Byron where he had this insight, which was and we can think of robots as media and since they are social robots it was like a form of social media. So, I got really excited and it was really my first big foray into working with robots and it was all because of Byron's insight of thinking of them as, as media and what follows from his insight then is most of the research on social robots looks at one robot at a time. There's a good reason for that. They're expensive, usually you've built a robot and you understand you know how does this robot you know evoke a response or how is it effective at getting people to learn or to, to feel better if it's an assistive context.

The problem with that which we know from psychology is if you're trying to generalize to social robots, studying one robot at a time is a real problem. This is a problem of stimulus sampling and so in psychology, we've known about this issue for many, many decades since the middle of the last century, and what Byron's insight sort of led to is that we need to get the whole a big collection of these stimuli so that we can start generalizing across social robots as a category of social actor rather than well there's this robot do anything or if we make this robot have an arm and versus no arm does it do anything?

And so that was what we were interested in is, is getting this big collection together so we could start doing research on a population — a sample if you will have social robots rather than one exemplar at a time.

Kim Mills: And so, what does this portend for the future? How will this be applied?

Jeff Hancock: Right, that's the key question and it's been exciting in this conference because I've already talked to, you know, half a dozen people that came up after the talk that were like hey we'd like to do this project or that project.

What we've done is started by asking well now that we have this collection of robots and a collection of photos of them. You know when you look at them and I can share a image with you to go on the podcast or a website, it's astonishing how varied they are. I mean, even when we show it to people that are in the field and been in the field for a decade, they're like well okay these are really, really different. I mean, it's sort of like thinking you know I could take you and study you as an example of an extrovert and then you know generalize to all the extroverts, but we know that people are really different. Well, robots are even more different than different people.

So, the first question then is do we needed a whole new psychology — a whole new side called you understand social responses to social robots?

And the answer when we look at the literature is pretty clearly no. People tend to bring sort of standard social psychological processes to new media. So, there's tons of work that shows that we treat technology kind of as social actors. And we bring our old brain, which has been evolving for a long time to understand social actors like you know is this a friend or a foe. We bring that to technology.

So, the next question then is well if we don't need a new psychology because people, you know, sort of react and perceive technology the same way they do humans, what's a good place to start to look at you know is there a fundamental dimension or two in which people perceive robots?

When we looked at the literature in social psychology around person perception, there's a lot of evidence that people judge others along two dimensions — very quickly, automatically, and, and you know, comprehensively.

So, their warmth and competence and some of the main research on this are Susan Fiske and her colleagues, Amy Cuddy, for example, they've done a tremendous amount of work showing that over you know a hundred years of research, across cultures people's perceptions, initial perceptions of other people really boil down to warmth, so is this person going to be trustworthy, kind, warm towards me or are they cold, perhaps threatening?

And they argue this is an evolutionary question — is this a friend or a foe? I need to determine that right away and then another is competence, so does this person seem capable, competitive, strong — these sort of terms.

And so, we thought let's, let's start there. Let's take a look at that and what we did is we, we had several thousand, over three thousand mechanical Turk participants take a look at a single robot and then answer a bunch of questions like does this person this robot seem warm or cold — bunch of those – bunch of ones related to competence a lot.

And then we did what you know psychological researchers do — you factor analyze those to see if they resolve to some factors and it's amazing Kim, it's exactly the same as if we…

Kim Mills: Just like people.

Jeff Hancock: It's just like people.

Kim Mills: So, what makes a robot warm or cold?

Jeff Hancock: Alright, so that was our next question exactly because designers are going to want to know this right like how do I make it warm or cold or competent or in common?

Warmth it turns out is really driven by eyes so does it have eyes or not, which you wouldn't normally think of right away is…

Kim Mills: But they don't. I mean Gebo doesn't have eyes, for example.

Jeff Hancock: Right exactly, exactly. So, there's this that's a major thing and not only anything about eyes so once you have eyes that's a big predictor. Then it's um the ratio of the eye size to your head size and there's lots of evidence that this is about warmth too. So, Disney characters for example, tend to have really big eyes. So, that's a really huge factor and then in terms of confidence it's the lack of fur. So, if you can see the mechanics — you know, like steel and you know actuators of that, they're going to actually appear more competent. If there's fur, they’re going to appear less confident and then mobility is a big one for confidence. If that thing can move around, whether it's you know arms or moving around like that, then there's more competence and so it's amazing and it actually has really fascinating and potentially disturbing implications.

So, Fisk and her colleagues have this model called a “stereotype content model” and they say with warmth and confidence, you can kind of predict in these in this 2d space stereotypes. So confident competent is up in the right. Those are people that are Earth's alright warm hi warm hi competent and that's the default in group. So, when they were doing their research in the early 2000s, late 90s, this would be like white middle-class America. So, if you ask Americans at that time you know the default group, the high confidence high warmth that was white middle-class men.

You go down into the lower space where it's high confidence little warmth, these are like engineers, rich people and these people are they evoke a different kind of emotion. So, it's envy right so and a little bit of so you're like you admire them a little bit, but it's more like a little suspicious. So, they evoke this envy thing whereas the default group evokes like admiration and positive emotions. You keep going around — so you're down a little low, low space and stereotypes down there would be poor people, so this be poor white, part black homeless people on welfare and they evoke a different kind of emotion, as well, which is contempt and so you keep going around — you get up to the high warmth low competence these are people in the 90s would be like housewives — people in that sort of space, you know mentally handicapped individuals, you know.

So, again these are stereotypes. And the emotion of oak there is pity, so as a designer, if you're designing a robot with these different features unbeknownst to you, you could actually be causing an initial emotional response that is deep-seated in our psychology.

Kim Mills: That's pretty interesting.

Jeff Hancock: Yeah, we thought so.

Kim Mills: Did this research tie-in at all with the work that you're doing on deception?

Jeff Hancock: Yeah, so now we're doing a bunch of things about like trust of robots, so how much would you trust this robot and the initial work there is that you know warmth is going to be a big predictor of that. Your sense of its status, but then we'll need to move it into different situations.

So, I might trust Gebo in an interpersonal interaction where we're just having a fun social interaction. But I might not trust Gebo if I'm on the battlefield and I need a robot to help me find bombs and defuse them, right?

So, situations are going to play a really huge role and our collection of photos really is an instead of a totally neutral like there's, there's zero context, so that's the next step.

But yeah, I'm really interested in deception with these robots. You know, one of my favorite examples of deception with technology wasn't a robot, but kind of similar and that's the Volkswagen scandal where they programmed their cars to lie to investigators who were looking for like how much pollution it would produce.

Kim Mills: Right.

Jeff Hancock: So, it's fascinating. This car when figured out that it was being tested changed its behavior. Right, like it would literally have less power but produce fewer emissions and not one car — millions of these and programmed to lie to humans and so I mean it's really fascinating and so…

Kim Mills: And so, you know the engineers use robotics when they were developing this?

Jeff Hancock: Well, we don't know that, but we can kind of think of the car a little bit like a robot. It's a technology that it wasn't making its own decision. It was programmed in but it was programmed by humans to lie to humans via you know it's, it's sort of technology, so right what's going to happen with robots?

We've seen some autonomous robots that have learned to lie so these are small little robots and their job is to go around and find food and they're competing with other little robots and the food is like a little electrical charge that they get. And they are given some artificial intelligence, so they're trying to find food but not let their other robots get the food and these robots would learn to lie. They would go to any area once they found where the electricity was they would then go to another area and buzz around there and then other robots would come and when they all came there and we see this with animals like crows are very good at doing deception so a younger male crow that will get beat up by the higher status crow will pretend to find food somewhere and then when all the big crow come it goes off.

Kim Mills: Right, right.

Jeff Hancock: So right, so we're going to be seeing humans using technology like robots to lie to other humans and we're seeing some of the very earliest evolution of deception in these, in these sort of artificial intelligence systems.

Kim Mills: Were those robots actually programmed to learn deception, so they just?

Jeff Hancock: They were given these constraints and objectives and the objective was to get as much of this food — their electricity, as possible and that they were competing with these other robots. And so, from that they learned that you know deception was a good tactic to do and we see this with, with non-physical AI so things like chatbox conversationally I in a negotiation game where they're negotiating with another human or with a human or with another AI, we saw that deception, that same kind of idea of deceiving evolved in that, as well.

So, it's pretty clearly an advantageous evolutionary strategy once you're able to communicate something that isn't necessarily true, then deception becomes a strategy for achieving your goals. It comes with risks, so if you're having a one-off interaction with another person, where you're trying to get goods from them, then deception can be very useful. But, over the long-term deception has been shown to not be necessarily the best strategy, so.

Kim Mills: Are we moving in any particular direction around the design of robots? I mean, I'm thinking are they going to become more human-like, less human-like or does it really depend on the context?

Jeff Hancock: Yeah, I mean that's a really great question and I think Justine Cassell, who did the keynote yesterday, I think sort of like wreath we asked that question which was it's not about what the robots or the these conversational agents and their humaneness necessarily, but rather about our humaneness and so she really put us into this concept of intercept subjectivity, which is when I feel like I'm engaging with the technology, I'm doing that as a human and I'm having a very human interaction, then whatever that agent is, is a success in that regard. So, it's about creating a sense of intercept subjectivity and I thought that was a really nice way of asking the question because then they can be human-like and they can be machine-like, but it's going to be about how that's sort of dyadic interaction works, so I think it's you know one of my intellectual heroes is Herb Clark, who's also at Stanford and his work shows that a lot of conversation and interaction communication is really tightly coupled. It's a joint action. So, what we're doing right now is very joint. So, we're nodding at each other and we're agreeing and suave the right time and looking and I know I'm in a very human activity. Right, is amazing joint activity of communication and so that's what's going to matter. I think with robots and with, you know, AI type technologies is the degree to which they're coordinating with us and building up that in your subjectivity.

Kim Mills: So, they could look kind of artificial still machine-like and exactly it will relate to them in a right way?

Jeff Hancock: Right. In theory, Brazil's Gebo and you know she gave the first keynote here, doesn't look human at all, but people really react to it right yeah oh she showed was amazing evocative right and then they're having this meal pleasant and intriguing and surprising kind of interaction and there's zero, you know appearance of humaneness, but Gebo has this ability to sense and respond in a way that feels very evocative. And it's kind of like you know people love dogs, right? And they don't look human at all, but you know people form these really deep bonds with them and it's because of that sense of intersubjectivity.

Kim Mills: Well, it's very interesting yeah thank you so much for being with us today.

Jeff Hancock: My pleasure, Kim. I really enjoyed it.

Kim Mills: Speaking of Psychology is part of the APA podcast network, which includes other great podcasts such as APA Journals Dialogue, about the latest and most exciting psychological research, and Progress Notes, which discusses the practice of psychology. You can find all APA podcasts on iTunes, Stitcher or wherever you get your podcasts. You can also go to our website, www.speakingofpsychology.org, to listen to more episodes and see more resources on the topics we discuss. I'm Kim Mills, with the American Psychological Association, and this is Speaking of Psychology.