Hello and welcome to the Behavioral Design Podcast.
This season we're diving into the intersection of behavioral
science and AI. We want to make sense of the
state of AI, from understanding how humans interact with
intelligent systems to using AI to do behavioral design itself.
I'm Aline Holsworth, a health tech advisor specializing in AI
and product design. Over the past 15 years, I've
been crafting human centered products with behavioral science
at the core. At Apple, I LED Behavioral
Science for Health AI, designing and launching AI powered
features to help users reach their health goals.
And I'm Samuel Sultzer, your second Co host.
I'm a behavioral strategist specializing in hybrid formation
and designing products that drive long term baby change.
I work with leading tech organizations integrating AI to
scale behavioral design for good.
And I'm also the founder of Baby Bites, a dedicated community on
behavioral science and AI. Quick word on Nuance Behavior
where we help organizations build impactful digital products
using behavioral design. We only take on a few clients at
a time to ensure the highest level of quality for our
tailored evidence based solutions.
If you'd like to become one of our special projects, e-mail us
at hello@nuancebehavior.com or we could call directly on our
website, nuancebehavior.com. Hey Aline, I'm curious if you've
played this game that I call the do but not recommend game if you
live with us? It sounds a little bit like you
just made this game up. I I somewhat did.
It reflects a little bit of my own behavior, navigating a world
filled with recommendation systems.
And what I mean here is like I do something, but the game is
for me to ensure that the platform I'm using is not
recommending me things based on what I do and so.
Because they're things that you don't want to do.
They're things that you wish you didn't do.
Yeah, like so on Twitter for example, I would say a lot of my
time is spent kind of reading about facts or things relating
to my interest in football. And so I for football club, I
get some news around that and and so on.
But I've made sure that in the feed I've made it so that I
never recommended any football facts or any any football news
and so on. So I had to act yeah, yeah.
You can use like block word, certain words, you can downvote
certain things, you can like depends on the platform.
You can kind of like say, don't recommend me this, don't
recommend me this. So I usually have to like I have
to go into the various accounts that I kind of still follow to
get the news. So I'm like playing this game of
like getting into to this because I see myself as someone
who's engaging with Twitter in order to stay up to date with
what's happening in AI and behavioral science and so on.
And not to stay on top of what's happening in football, even
though that's what I often times do.
So I'm I'm playing this game and I'm curious if you can relate to
it or? Yeah, in some ways it sounds
like a pretty nice commitment device, right?
Like you're adding all this extra friction to getting the
information about your soccer. I'm just going to say soccer.
And, and maybe you don't consume as much soccer as you would
otherwise, which, you know, maybe it's part of the goal,
maybe it's not. But honestly, when it comes to
Twitter or X itself, I pretty much stopped using it when it
became X because I, the recommendations that I got were,
were so far off from what I was interested in.
I felt like I had for many years really sort of, I don't know,
led the algorithm towards things that I was really interested in.
It was purely academic. It was it just like my
colleagues sharing their new findings and, you know, adding
the TLDRS about it and just just really, really useful summaries
and commentary about what would have been published.
And that at some point all of those disappeared and I just got
political commentary and, and things that I'm just so, so, so
uninterested in or lately I don't, I don't want the, the X
version of that, let's say. But, but going back to your
original question, do I, do I do any of that manipulation?
I actually do a much lamer version of that, which is I just
exhibit self-control in the moment, so when I see.
Something. I do, yeah.
I actively stop myself. I say to myself, I see something
that I that I don't want to be. I don't want that view to be
incorporated into into my algorithm.
I look at it and I say don't do it, that's going to work against
you. And then I actually don't look
at it as much as I want to. I I tell myself, Nope, you're
going to be seeing those stupid videos for the rest of your
life. Yeah, but then you're like,
aware of the consequences of like, actually watching this
video will have some ramifications.
But I don't miss it. I, I think that the difference
is that those, the videos that I'm tempted to look at are not
something that I, I really care about keeping up on.
Whereas for you with your, with your silly sports, you do, you
do care about keeping track of that thing.
For me, it's like parenting videos, you know, like people,
people telling you it's all right, you're going to get
through this and here's why parenting is so hard.
Like you don't really need to see some random stranger with a
million followers telling me that that's not how I want to
spend my time. So yeah, that that has been my
solution. That's fair.
And I guess that's the last point on this from from my, I
guess I would love to say that my motivation was that it's like
some form of commitment advice in a positive way.
But I think it's a little bit of like some form of Dorian Gray
thing in terms of, you know, how in Dorian Gray is this person
who lives a life where he's beautiful, but he has like a
image of himself that becomes ugly and ugly and uglier in
hiding. And I feel like what I don't
want to see is when I open my feed is to be reminded of the
ugliness, like in terms of my behavior.
I don't want to see that I am someone interested in.
Turn the mirror. Away some other things and so I
think kind of ensuring some form of yeah, nice image of myself is
probably why I do it. You're you're sweeping ugly Sam
under the rug, hiding him so that you can believe you.
You can even fool yourself into believing that that this is that
ideal Sam is Sam. Wow, interesting.
All right, cool. I love talking about recommender
systems and I, I also love how the algorithm has just become
part of like general conversation.
Like everyone understands that they're being fed
recommendations based on what they've seen or what they've
done. And, and that, that this is just
a part of everyday language. Like what, what you're doing to
modify, to control or not control your algorithm.
It's this is not even niche language anymore.
No, we're way past that point where we said like, oh, isn't it
creepy that what I do online impacts what I see and stuff
like that. It's just that's just so 2010 or
15 or something. Yeah, pretty much.
Well, and it's funny because even though this, this is has
become so familiar to all of us, what I didn't realize until a
couple years ago was just how complicated these systems are.
And Oh my gosh, until I, I was really like in the weeds of
working with recommender systems.
I just, I just didn't have an appreciation for the math and
the engineering and the the complexity behind them.
You had some, you had some first time experience.
In, in I did. Yeah, yeah, I worked.
I worked quite a bit with Recommender Systems.
I won't say where, but but yeah, I thought it might be useful to
do a very high level overview of, you know, if you like take a
recommender system course that that you always get the, the
sort of these are the types of recommender systems.
I thought I'd do that for us today for our behavioral
scientists audience who you know, probably hasn't come into
into contact with this. But I think it'll be also useful
for our conversation with Carrie where we really get into the
types of data and how preferences are modelled.
So when we, when we think about our recommender systems, one O
1, there are two major categories and how they work.
We have content based filtering, which where each user basically
a profile is created for them based on what they say they're
interested in. So for you, Sam, this would just
be your your academic articles on Twitter.
And then that information is matched to content in order to
provide recommendations to to users.
So there's type 1 and then type 2 is the collaborative
filtering. And this is I think often what
we think of as recommender systems.
And that's because it is more commonly used.
And this is where you, you're really relying on people's
implicit data. So what there's what they're
viewing, what they're clicking on.
And, and this would be if you didn't override the system, this
would be your, your soccer content.
So they're basically inferred preferences.
We, you know the we, we the algorithm, think that you prefer
watching videos of soccer because that's what we see you
secretly doing at night with your flashlight under the
covers. Yeah.
And I guess this is what I would say of all of the different
algorithms and recognition systems, I think TikTok is
probably the one that has become most kind of famous or infamous
for this type of kind of not only relying on kind of what we
click on, but use like the time spent looking at something.
I do feel like the the vernacular of train the
algorithm actually really did come out of the early TikTok
days where people realized it was just so blatant and obvious.
If you watched one video that you know, the you would be
served up another video just like that one right after.
So it was, it's just so blatantly obvious.
I think that's sort of is when it entered our vocabulary
awesome content based and collaborative filtering, two
major categories. Of course most recommender
systems that we have today are some combination of those and we
call those hybrid models. Yeah.
And so those are major types. One thing that it that I think
is maybe interesting about the types as well is just that there
is so much more implicit data available.
And so just as a result of that surplus, you know, you, you kind
of can only use the data that you have.
In some ways that ends up being what companies rely on in order
to make the recommendations, but it also tends to be more
predictive of time spent on the platform.
So if that's what you're you're trying to optimize, which in
many cases unfortunately is you, then you might be sending more
gratifying content that is that's going to keep people,
keep people clicking and viewing and scrolling and so on.
Yeah, I know this is such an interesting subject and I'm so
happy we had a chance to to speak with with Carrie about
this. So maybe, yeah, do you want to
introduce Carrie? I would love to.
So we have a very special guest today, Carrie Morwich.
And for me personally, Carrie is really the person who took what
was purely an academic love of behavioral science and showed me
that that this could actually be a career, not just a passion.
And so Carrie was was really my very first real mentor, the
first person who kind of picked me under his wing and showed me
the way. I met him when I was an intern
actually at Carnegie Mellon way back in.
And I'm not sure I should date myself, but back in 2008.
And I was so excited by the work that that we did together during
this internship that I actually asked him to be an advisor on my
thesis. So he agreed.
We, we get to work together and just the, the amount that I
learned from him in that period is, is unparalleled.
And I, I remember this moment, I think we were eating lunch
somewhere and we were, we were talking about my thesis work and
just just totally out of thin air, he pulled out this, a full
citation for some statistical analysis that we were talking
about. And I was, I was just blown
away. My, I'm just completely
awestruck. Like, like this guy is pure
genius. Like how, how is he doing that?
And yeah, Carrie really taught me what it what I think it takes
to become an expert at something.
So if we Fast forward to today, Carrie is a professor of
marketing at Boston University. Here he he continues to strike
on his students and to research decision making.
Yeah, well, I I first of all, I love this idea of having in your
back pocket a cool citation to to impress people with but but
also I'm interested. So tell us more about what does
it mean here in terms of research decision making in the
context of Carrie? Well, first I'll say I don't
think it was intentional. I think that is that just comes
naturally to some people. I certainly don't have my own
version of that, but yeah, so does.
Yeah. What does it mean to to study
decision making? It's actually kind of hard to
describe Kerry's research since it spans so many different
categories. If you sort of take a look at
his early work on affective forecasting with Dan Gilbert and
Tim Wilson, some very amusing research on consumption
behavior. And and then, you know, he's
really, he's also covered all of what we consider the the
behavioral economics classic, right?
Like so mental accounting, anchoring, confirmation bias.
He's looked at the endowment effect, actually teasing it
apart from loss aversion, because some people kind of
treat these as as two sides of the same coin.
But Kerry will tell you they are.
They're not. And today, very excitingly, he's
one of the most prominent researchers looking at how
humans interact with AI systems. Which, of course, is what this
episode is all about. Yes, I'm giving you a quick
preview of this episode. We look at AI systems from
perspective of Kerr's work and especially kind of understanding
this idea around the complexity of human behavior.
And that if we don't understand that, this is to fall into these
three common pitfalls that can lead to misalignment within AI
recommendation systems and honestly so much more.
In this episode, there's so much that we cover, including
strategy and carry to our quick fire round of the season.
To AI or not to AI and asking for his most controversial
opinion about AI. Oh wow.
Carrie, welcome. It's wonderful to have you on
the show. Thank you for having me.
Such a pleasure. It's been like a lifetime since
we've hung out. It does feel that way.
Our our beers in in Pittsburgh have gotten warm by now.
At least our children's life. That's.
Right. And for me, I think it's been a
lifetime in terms of we haven't had a chance to meet before.
So very excited to to shout. Me too, thanks for having me.
So, Carrie, this season we're really focusing on AI and what
behavioral science can say about AI.
And you of course, have done some very exciting work at how
people think about AI systems, how we perceive them, when we
trust them and when we don't trust them, but also how these
systems that are supposed to be these ultimate prediction
machines, right? They can actually get it very
wrong sometimes. And, and perhaps that could be
because they're solving for the wrong goals or, or they don't
have the, this, this understanding of human behavior
built into them, or, or at least the, the machine learning
engineers who are devising them have not thought about how
humans think and behave. And, and so one of the points
that comes through in your research is that when, when
these AI systems base their recommendations, So in a
recommender system, for example, when they base those
recommendations on users digital footprints.
So what was viewed or clicked or bought all all of those
behaviors that are tangible and and seen captured by the system.
When the systems are, are designed in this way, they they
ignore what's arguably the most human thing about humans, right?
And that's the gap between our intentions and our actions.
So the the behaviors that we exhibit may not necessarily map
to the things that we actually want to do, the ideal humans
that we see ourselves as. So what I'm hoping you can dive
into now is, is like what are algorithms doing wrong when
they're basing their recommendations on users digital
footprints? Right.
So that's it. It's an amazing lead in and
maybe it's useful to begin by thinking about like what they're
trying to solve and how they do that.
Let's begin by thinking about it as a spreadsheet, right?
So an algorithm, you're telling it here are some columns to
consider in the spreadsheet, right?
I want you to use the information in those columns to
make some kind of prediction. And like if you're thinking
about it as regression, like those are your like X variables
and the prediction is AY or outcome, right, that you're
trying to predict. And then the data is really like
the rows, right? So the users are the rows right
in that spreadsheet. And so as algorithm designers,
right, you're trying to think about, OK, well, what columns
should I include or exclude in these kinds of predictions?
Do I want to just like take all the data I have and like try to
model something and see what it is?
Do I think that there's like a certain number of columns that
does something and then I'm trying to predict that?
And then you're also trying to think about like, what is the
thing that you're trying to predict and what's the best
representation of that, right. So if I want to think about, for
example, let's say that you are Netflix, right?
And you want to show Samuel movies that he will enjoy,
right? Like the outcome you're trying
to predict is Samuel's enjoyment of the movies you want to
recommend movies that he'll enjoy.
So he stays on the platform, spends more time there, keeps
his subscription. What kinds of features of films
do you need to include in your prediction to like get a good
prediction about movies that Samuel will like?
Is it about like the actors, the genre, the length, right, like
the visual representation, the language?
So there are lots of different kinds of features, the director,
right? Like there's lots of different
kinds of features that you can include in that kind of
prediction, right? And then the.
Then you're trying to think about like, OK, well, what does
it mean for Samuel to like the movie?
So is it that he finishes the movie?
Is that he finishes and rates the movie positively?
Like, you get that like or love, right?
Yeah. Is that like, or should we just
use like or love? Like even if he watches the
movie and he doesn't like or love it, like then should we not
say that he liked the movie? I mean, I've had many
experiences on Netflix where I ended up watching a whole thing
and I was like, that was terrible, that.
Was a crappy movie. Yeah, exactly.
So there's a lot of different kinds of assumptions that we're
making when we tried to take some abstract concept like
enjoyment of a movie. How much will Samuel enjoy the
movie, right. The idea behind that is that
we're using, we're observing his behavior.
So how much of the film, which movies he chooses, which movies
he watches, how much of the movie he watches, how he rates
it, right? We're observing his behavior to
infer how he feels about the movie.
And Economist would call these two kinds of states that we're
using revealed preferences. So what he's doing, what he
reveals about his behavior to infer people's normative
preferences, what they actually believe.
So let's take an example from Amazon.
So many of us use Amazon for like as current for most of our
purchases, right? Yeah, so.
It's not returned. Yeah.
So let's say that most people don't rate the things they buy
on Amazon, but they might. Let's say that you buy diapers
on Amazon for your child there. We could say the purchase is 1
signal of liking the diaper and then repeat purchase would be a
stronger signal of liking that diaper.
It's harder to do something with a lamp which you buy and either
return or you don't. The return is a clear signal.
I don't like this lamp. Buying the lamp and not
returning it probably means that you like it, but we don't know
how much you like it. Do you buy, I think, other
things from that brand, or do you go back and look at other
lamps after you buy it? Right.
So it's a really difficult problem because even with big
data, we don't have a direct conduit into people's mental
states. There's a lot of missing data.
Yeah, there's a lot of missing data there.
And there's a lot of assumptions that we're making about like
what people are doing and what it means.
And so the what people are doing are the revealed preferences and
the what it means. Like, do we actually enjoy
watching, you know, Love Island, right?
Is that something that I would enjoy watching?
Yes. Is that something that I would
be happy about watching tomorrow?
Those are different questions, right?
Oh, and do you see yourself as the kind of person who watches
Love Island and then the, you know, sort of the, the enjoyment
while you're watching it versus the regret you may feel later
after, you know, however many seasons of binging it?
Yeah. Are are we still talking about
me as a as an example? So here's an example.
And it's not always that people are trying to pursue pleasure,
right? So reading the New York Times is
like a factory of sadness, right?
It makes me so sad to read the New York Times, but I feel
obligated to do it as a citizen, right?
But it's something that like, I feel afterward, I feel good that
I know what's happening in the world.
Although if you like looked at how much, like if you were to
measure my affective state while I was reading New York Times, I
feel very unhappy most of the time.
You know, So like, there are different kinds of goals that
people have. There's a really amazing data
set from the Neely Center at USC and they look at like people's
emotional states while they're engaging in different kinds of
social media. And like YouTube is one of the
most positive kinds of experiences.
And you could think about it like it's not necessarily a
platform's design, but like why people go to YouTube is to
learn, right? Like you go to learn about
strollers or cars or like how to fix your refrigerator, right?
Like that's like you're, you're learning things and it's kind of
interesting, right? Like Twitter and like next door
are like the least positive platforms, but like there you're
kind of going there for like things that will make you
unhappy, Like, and so there's a lot of kinds of assumptions that
we're making when we're looking at people's behavior about like
what the purpose of what they're doing is like what are they
trying to optimize on? And so is the platform
optimizing on what the user wants to optimize on in that
situation. So that's like a long winded
version of like what the difference between like
normative preferences, like what I'm trying to optimize on as a
user and then reveal preferences the platform trying to infer or
what am I trying to do? And how well does my behavior
predict what they think I'm trying to do?
And yeah, and, and there are different ways of getting at
these different versions of people's preferences, right?
Do you think there there's a way to have your cake and eat it
too? And, and thinking about, you
know, how if you just take the Netflix example, you have
multiple shelves, right? And you can have your like, you
know, classics in one, which maybe is the version of you that
you want to be. And then you have your Love
Island shelf of, you know, all the trashy reality shows.
Do you think that these multiple shelves can coexist or that, you
know, will they just be overtaken by the algorithm
learning over time when you continue watching Love Island
that the classics kind of get pushed lower and lower into your
recommendations? Yeah.
I mean, I think that's a huge problem because it's hard for
the platforms to observe like the heterogeneity of our
preferences. And some platforms do do that,
like Netflix does try to cluster different kinds of preferences
that we have and they're building up like.
But a lot of that is called from like collaborative filtering,
which is it's showing us things that people who like the
particular movie also. Yeah, Someone Like You who
watched similar things would all.
Here's something else that they watched and enjoyed.
I, I think the a big problem is that our preferences are much
like a, a Senate right or a, a, a group of people like a, a
collective right. We have different preferences
and sometimes they conflict. And which preference is dominant
in a particular situation often depends on the goal we're trying
to pursue or where we are in some particular context, right?
You could think about at a family level, we need different
Netflix accounts to make reasonable predictions.
I have a friend who shares an account with his children and
his wife. And it's like there's no
coherence to what it's because like it could include like Coco
Melon and like, you know. Spotify has that problem as
well. Yeah, some like, you know,
Scorsese film and like you wouldn't suggest like, you know,
Scorsese film to a child. So, but like in our in, in my
family, we have 3 accounts, right?
One for my wife, one for me and one for our children.
There's more, more coherence there.
They, they, there's still some heterogeneity too.
Yeah, but. Even within individual
preferences, there's a lot of variety, right?
Yeah. And if I recall, there was a
point maybe early on in the Netflix days where Netflix asked
people what kind of films would you like to see?
And so it really directly got at their stated preferences.
But that went so poorly because people didn't actually want to
watch the films that they said they wanted to watch.
And so they, you know, they lost users and so on.
So 1 is this issue right? So Katie Milkman and Todd Rogers
and Max Bazerman have a lovely paper called, I think Highbrow
Films Gather Dust. And the basic idea there is
they're looking at what movies people put into their watch
lists. And the canonical example is
like, Schindler's List, right? Most people feel like obligated
to watch Schindler's List or think they should want to watch
Schindler's List, right? Or movies like that that are
like historical documentaries that are kind of sad, right?
And then what do people actually watch, right?
I think like the top movie in Netflix was like Red, which is
like an action movie, right? So, you know, our extraction is
also very popular in Netflix. When we're thinking about our
choices, like what do we think are the movies that we should be
watching? We're putting those things in in
our watch list. And they're probably like
classic movies or movies that are emotionally difficult to
watch, things that require attention, things that will make
us feel like better people and that we would be be proud of the
next day having watched or spent that time that way, right?
But when we look at when people log on and like you're exhausted
from work and you're trying to pick something, you don't want
to do that, right? Like.
Exactly. That's that's like a eating your
like you've already eaten your vegetables for the day, like
you're, you're ready for dessert, right?
So, you know, so in, in those kinds of cases, then people are
watching more lowbrow films, right?
So they're watching like comedies and action films and
whatever is going to be more like emotionally palatable to
them. And so this, there's this really
big problem about platforms. This isn't just a Netflix
problem. Platforms more generally are
observing people's choices while they're making the choice.
And that sounds very reasonable, right?
Like I want to see what people choose when they choose and I'm
going to show them more things that they choose.
The the problem there is that like a lot of platforms then end
up recording the weakest point in our self-control conflicts.
Instead of reminding me that I should be watching these other,
I want to watch these other things or I should feel like I
should be watching these other things.
And it's not measuring like how I felt about the movie I watched
last night. It's like, how did you enjoy
this movie right now, right? And like, do you go back and
watch that movie again, right? Or do you watch other movies
like that? So, you know, if you keep
feeding people, you know dessert, right?
And you're like, people eat dessert.
Like if I put dessert in front of my kids and they eat dessert,
right? And then you're like, oh, my
kids like dessert, I'll give them more dessert, right?
So like you keep doing that, like they'll keep eating it.
But like, that's not to mean that like they wouldn't eat an
apple if you put it in front of them, right?
Or like they don't just want to eat dessert.
They might like eat some apples and some strawberries or
whatever, right? So the platforms even in like
say, like, let's go to like an example, like Twitter, right?
Or you know, and there or like TikTok or something like that,
right? So they're looking at what
you're consuming then. And like in Twitter, like you
might be interested in like a salacious partisan news story,
like JD Vance being like attracted to furniture, right?
Like that might be funny, but that's not like, and you might
like, look at it while you're scrolling, right.
But like, that's not like what you.
If the platform is all that, then people are done.
Oh, yeah. And so the yeah, like, so like
the the real problem with these kinds of platforms.
And I think you can see it illustrated with like, sort of
like the problem that Twitter's algorithm has had since it was
like, acquired and became acts, right?
Is that like, if you get too much into the want space, your
platform is basically becoming like, like a flight from
Pornhub, Right? Yeah.
And like, no, seriously. And like, it's like people,
people will eventually exit the platform if they don't feel good
about like the next day about having how they spent their
time. They might spend a lot of time
on your platform when they're there because it's like shocking
and salacious or like it's like, it's like emotionally evocative.
And maybe they, maybe it's like moral outrage that they're
feeling right. Or maybe it's like pleasure, or
like some kind of hedonistic experience, which is fine to
have, but you need a balance. Yeah.
And I think that that is really the most critical piece of the
puzzle, right? Is this impact on long term
outcomes, both for the person's well-being and how they, you
know, the relationship they have with the the product, but also,
you know, for the for the company that's trying to get
users to stay. If they're only looking at these
short term outcomes and behaviors of what people watch,
then they're not understanding the fact that in the long term
this is actually bad for business because people are not
happy with their behaviors long term.
Yeah, and that's like one solution is looking at like
let's just track behavior more long term, right rather than
like what are people clicking on like what's what's the immediate
kind of response, right. So what how long is it till
people come back to the platform?
Yeah, right. What is their like frequency on
on time look like over a month? Also like can you look at when
people are making choices, but like in an interval of time
before they consume something like the watch list, right,
right. Is there a gap between what
they're putting in that watch list and what they end up
consuming? And if there is, then there may
be ways to try to think about how do we reconfigure the
recommendation system So it's reflecting both kinds of
desires. Because if you make a platform
all shoulds, people won't log into it because there's no
demand. People will not want to do that.
But if you make a platform all wants, then people don't feel
good about the experiences that they're having later on and
whether or not it's detrimental for their emotional or physical
well-being. We can put that aside.
People just may not feel like that represents their
preferences. A broad problem too, like
whether or not we're actually modeling wants or shoulds, is
that a lot of the kinds of decisions that people are making
on these platforms are like habitual or they're done very
intuitively. People are not like deliberating
a long time before they're making these kinds of choices,
right? And so my decision to click on a
news story is like, you know, does the picture and like,
however many characters look interesting.
Does it look interesting from that point, Right.
I'm not like, who is the poster? How many likes are there?
How many retweets, right? How many comments are there?
People do that when they're thinking very carefully about
like, is this true or not? Right, but that.
Sometimes. And like, and also like that.
So there's like one level of people are not like fully
thinking about all of the information.
They're making more of an intuitive judgment than a
deliberative judgment. And so I get picking more up on
like how people think quickly than how people think carefully.
And the other sort of thing is that like, you know, platforms
run a lot of AB tests, but they don't make like major structural
differences in the tests that they present to users.
So we don't know like how much context is there.
So we're then like, what we end up with is we're inferring
people's preferences from like limited attention, right, from
habits, and then like within a particular decision context that
could skew their behavior. So let me give you an example of
like why each of these is a problem, right?
Habitual behavior. If we just observed smokers and
like saw how they were smoking, we would think that like, most
people who are smoking enjoy smoking and like, don't want to
quit, right? If you survey most smokers, most
smokers say that they do want to quit, right?
So like, if we're looking at some kind of behavior that's
habitual, like most people, I, I would get gather spend.
Like the data suggests that most people are spending about an
hour on like a platform like Instagram.
I've been in a where I saw someone from Meta show a slide
where they went from like the average user in India went from
like 20 minutes a day to like 40 minutes a day.
And they were like excited about that, right?
That's great for Meta, but they thought the user was having a
great time on Facebook too. I don't think anyone consciously
wants to spend an hour a day on Instagram or an hour a day on
Facebook and think like that's a good use of their time.
I think if you ask people like how much time would you like to
spend on these platforms, they would be like something like
maybe like half an hour Max. But most people would probably
say like, I want to spend like 5 to 10 minutes, right?
I definitely want to spend, I don't want to spend more time on
this platform than I spend with my children, right?
Or like more time on this platform than I spend, like
eating with my family, right? Yeah.
So I guess we might want to jump to another topic, but before
then I give you a chance to summarize the, you know, when we
talk now quite a lot about the various kind of aspects of
recommendations engine and algorithms to support us to make
some more better decisions in some ways.
What are the big problems that you've covered so far?
Yeah. So there's sort of three, right.
We're looking at thinking fast, not thinking slow.
And that would be inattention habits and, you know, context
effects on preferences. Algorithms are observing wants,
not shoulds, right? What we want now, right now and
not what we think we should have chosen or should choose in the
future. And then algorithms are
prioritizing like popular options and the status quo, like
what we have data on, what most people like, right?
And those kinds of three features are not necessarily
inherently bad, but like they may lead us to make inferences
about what users, users normative preferences, what they
think, what they actually want, right, That are inaccurate
because we're looking at the revealed behavior.
So if I just look at what you're choosing intuitively, I may pick
up on habits not like, you know, I see that you smoke and think
that you like smoking, right? If I look at what you want, I
might think that you don't like more highbrow films.
You really like just lowbrow films.
If I look at what you choose from like a recommendation
system, right, that tends to prioritize popular options, I
might think that, you know, you like Taylor Swift more than
this, like other indie artists, right?
Then, you know, you mentioned looking at long term behavior.
That's a big kind of thing. Another thing that we didn't
talk about is there are users who are modeling the behaviors
that you would like users to model on that platform.
Like there are people on Twitter who are reading interesting
articles that are true, right? Or carrying like comedians that
are telling funny jokes that everyone likes, right?
You can teach or train the platform on that subset of users
who are behaving the way that you would like users to behave.
The analogy is let's take like we were way MO and we're making
self driving cars and the way that you train a self driving
car is it observes how a human would drive.
Now, let's say that you wanted to train driving cars and you
train them in Boston. Boston is full of terrible
drivers, like self included, right?
Like we do all kinds of terrible things like you have to break
the law to drive in Boston. So like it's just impossible
otherwise. So should we train self driving
cars on the population? No, we want self driving cars to
be trained on people who'd have a zero accident history, right?
Canadians. You know, yeah, like nice
drivers. We want like more self driving
cars to be better versions of ourselves, right?
And so like we kind of like an idea like why don't we train
platforms on those better versions of ourselves or the
users that we hope the platform was best served right?
So one maybe quick thing that I want to touch upon before we
jump into our quick fire round of the season is something that
you've explored quite a bit, which is somewhat related to
what we covered already, which has to do with algorithm
aversion. So it's interesting to see how
people react to algorithms and and what levels people feel
comfortable with that and not comfortable and why and so on.
Sure. I mean, I think a lot of like,
you know, for the most part, algorithms are doing an amazing
job in much of our lives. Like we've been spending the
last however long talking about some of the problems with these
algorithms. But like on the whole,
algorithms are like dramatically improving our lives in many
dimensions, right? So for example, you know, being
able to, I can find the best route through Boston traffic at
any time of day or night, right? And I know all the routes from
like my office to my child's, like school or from my office to
my home. But like an algorithm can guide
me through the optimal route at any time of day or night.
Like that's amazing. It can shave like 10 minutes off
of my commute. That's, that's fantastic, right?
Like, I think algorithms are also amazing at, like, solving
like, the LA problem, which is when my cousins and my
grandparents lived in Los Angeles and like, when I used to
go there in college, like in the 1990s, which is, I shouldn't
admit that. But like, like LA is a city
where, like, the most amazing sushi place you could ever find
is in, like, a strip mall on, like, in some weird place,
right. Like, you need to know.
You need to know that, like, that sushi place is in this
place or you would drive by it and forever never see it.
Never. Yeah.
And like all the best like bars and clubs or like, you know,
different kinds of social activities are all like, there's
like, it's very hard to infer like what is good from just like
casual observation, right? You need to have local knowledge
and these algorithms. Now I can like go to LA and I
just pull up Google Maps and I'm like, OK, find me find the best
Mexican restaurant like in Westwood, right?
Like or in North Hollywood or whatever.
And it's gonna like, it would give me, like it gives me that
note local knowledge immediately, right?
So it's like aggregating all this information that like we
didn't have before. So algorithms are amazing on all
these kinds of dimensions. So, and we use algorithms too,
Like, so you know, if Sandra, like if you came to Boston and
Alien came to Boston and you were, you know, at a hotel and
you were like, I'd like to go see Fenway Park because I'm
really curious about where the Red Sox play, right?
And you go downstairs and there's a hotel concierge and
that person is an expert on Boston, right?
They know where Fenway is. They like have maps, they can
tell you how to get there, right?
My guess is you walk right by that concierge and like check
out Google Maps, right? So even if the concierge told
you how to get there, you'd probably still open Google Maps,
right? So we have in many kinds of
cases, preferences for algorithms over people.
Like the. So Google Maps example is the
one that rings true all the time, right?
I would rather use Google Maps than any human direction, right?
Always. But there are also domains in
which we don't want algorithms in lieu of people.
For example, I would have a strong aversion to my son first
romantic relationship be with an algorithm or a chat bot rather
than a human being, right? I would feel despondent as a
parent if like yes, his first love was a chat bot, right?
Like that was feel terrible, right?
And so that doesn't seem like a good substitute for like that
human capacity, right? And I think a lot of people more
immediately worry about algorithms like doing their
work, right. And in some cases, like we don't
necessarily like care. So people used to do statistics
by hand. Now people use statistical
packages, which are algorithms. They'll do their statistics.
And I don't feel threatened as a professor that there's a
computer program that does statistics.
That's amazing. I don't have to do this part of
the work, right? But those are the things that
people necessarily don't identify with as their
competency. And So what I think is when we
really feel uncomfortable about algorithms doing something, it's
some kind of competency that is important to who we are, our
identity, right? And so I might not care about
using a statistical package because for me, it's like the
theory or the idea that's important.
But for a statistician, they might be like, wait a minute, I
don't want ChatGPT doing all of your statistics, right?
Because I have important kinds of ideas or whatever, right?
So, yeah. And.
I wonder how much of that boils down to the fear of losing your
job, right. You might be happy to use, you
know, AI tools to accomplish your tasks as long as you're the
primary one being paid and not losing your job.
That's one of the biggest threats that people face, but
they're also resistance to automation, which is fueled by
algorithms that people have where it's more about things
that they enjoy doing that they don't want to outsource.
So Stefano Puentoni has this amazing paper where like, I
don't enjoy baking bread, right? So if I had to make a lot of
bread, I get a bread maker. There are a ton of people during
the pandemic who really enjoyed making sourdough, right?
Those people would not want to buy an automated bread maker.
They want to knead the dough and bake it and observe what
happens, right? I love cars.
I have a manual transmission car cuz I enjoy having that kind of
control right? To me an automatic is not as fun
to drive cuz I have less control over it and the self driving car
sounds terrible. Whereas there are many people
who like driving is just a chore and they want an appliance that
gets from one point to another and they don't really enjoy that
kind of experience and so or having any control over it, they
just don't want it to crash right?
And so for them, an automated car would be delightful.
And so in these kinds of spaces, it's really sort of how much do
you identify with the thing that the algorithm would be doing?
And is that a compliment to like what you're doing?
Does it enable you to do something more easily and like
more fluidly, like the statistics package?
Or is it replacing like a core competency that you identify
with, whether it's like making bread or like driving or writing
screenplays or writing the news? Well, that seems like the
perfect segue. Since our quick fire round is
all about good or bad uses of AI.
We are now going to share a a number of activities and ask
you, Carrie, to tell us to AI or not to AI.
Well, do it. Predict the outcome of a
presidential election. Should but not there yet cuz
there's so much change in historical context and there's
not enough data that I I think that's an area where AI doesn't
do well now but sure in the future why not?
How about predict outcome on the football game?
To AI or not to AI? Totally, yes, definitely not.
Early season maybe, but later in the season it's gonna get more
accurate. That's right, organize a summer
internship program. What kind of summer internship
program? Behavioral decision research,
for example. Sure.
I mean, if there is, if there are lots of good models to
emulate, why not? What about the sign A better
motorcycle? Sure, this is my favorite.
Devise a diet plan using imagined satiety.
Why not? Why not?
This is when you came out with this paper, Thought for Food,
where you show that people can habituate through imagining
eating foods and desire those foods less often.
I used to always think in my head about how I could create my
own diet of just imagining that I'm eating any time I was
inclined to lose a couple pounds.
Like no need. Like take your reminder, like
think about eating this. Exactly.
Are you happy? No, yeah, that that could work.
I, I, I would be excited to see a test of that.
Well the human version of me never came up with the actual
diet plan, so I I guess we'll have to rely on AI to do it.
Yeah, but I mean, AI is amazing for things like putting a meal
plan together, you know, devising an exercise routine.
You know, I think it all depends on like the quality of the data
out there for it's great. If it.
Saves us from like if it saves us from the blog post Intro to
the Imaginary Diet plan or like every recipe now gives you like
the history of bread. Nice.
So I think you mentioned maybe it was when we started the the
podcast that you have two children and so this is a
relevant one for, for you read a bedtime story to a child to AI
or not AI. As a parent I feel I should do
that, but there are times when I can't so I would say sure.
Better than nothing. Yeah.
All right. Act as human participants in
experiments. I don't think we're there yet.
But you think we will be. I think, you know, we do
simulations like, there's lots of like game theoretic
simulations. I think it can get there, and
it's probably useful for like highlighting a concept, But you
know, there's a lot of work suggesting that it's just
recovering data that's out there so you can get basic kinds of
people's response or demand to like price curves and things
like that, that resemble what humans would do.
But I think a lot of that's like scraping prices.
So I think we'll get there and it can be a reasonable test run
of like let's say you want to run a big field study.
You can do a simulation with your program, but I don't think
we're there yet. OK, final one, diagnose skin
cancer. Yes, it's not as good as a
dermatologist yet, but I think it'll get there.
Maybe AI plus dermatologist is the right combo.
I think that would be better than just a dermatologist.
Yeah. And that brings us to maybe the
final question, which kind of maybe indicates as well kind of
your stance on AI. What is your most controversial
opinion about AII? Think my biggest concern with
AI. I'm not sure I have a
controversial opinion about AI, but I would say my biggest
concern is like it replacing human.
Like large scale replacement of human interaction.
Just people at home having relationships with a IS.
And not that there's anything wrong with that from a
philosophical or logical perspective, but it seems like a
way to escape fundamental aspects of the human experience.
You're really concerned about your son's the first
relationship? No, I mean in general, like it's
like a is are going to put up with our personality quirks and
like grumpy moods, like more open, they're gonna be able to
tailor a conversation to what we would find exciting.
And like there's part of the human experience that is being
validated and like feeling like your emotions and ideas and
opinions are valid. And that's an important aspect.
And I think that they will play to that.
But I'm not sure that maybe they'll get there.
But like I'm I'm worried that they will also include the kinds
of challenging conversations or differences of ideas or opinions
that interacting with other humans you know involves.
Are you familiar with the company replica?
Yes. I think one of the interesting
subreddits out there that I randomly came into it at some
point because I was interested with Replica being kind of a
little bit of like before the very recent AI hype, they were
already somewhat established as some form of AI friend platform.
And they have a pretty big Replica subreddit, for example.
And he's reading about people's kind of experiences from losing
their account or talking about celebrating anniversaries and
all of these things with these. I guess replicas has created
mixed feelings. So yeah, I'm just curious if you
had any thoughts. Yeah, I mean, I think that that
there's a use for those kinds of proxies or parasocial
relationships. One could be teaching people how
to have different kinds of conversations.
Like let's say you want to have a conversation with your child's
teacher about an issue, right? Or your family member's sick.
And like you want to think about how can I convey how I feel in a
way that's sympathetic and empathetic to them?
Or maybe you and your spouse are trying to negotiate what to do
in the backyard, like what kind of landscaping to do, right?
You could think about these kinds of platforms as mediation
tools or teaching tools or simulation tools.
And so for example, as department chair, I have to send
a ton of different kinds of emails, right?
And if I have an e-mail that I think could be particularly
important to a variety of people and needs to be particularly
sensitive in the way that I'm expressing information, I'll run
it through like ChatGPT or Google Gemini or a Claude just
to see, you know, is there a more positive or optimal tone
for this e-mail or can I improve it?
Right. So I think just as we can do
that kind of simulation run, we're copy editing on the things
that we're doing, having that kind of chance to simulate new
or challenging or particularly important kinds of experiences
and get training through that kind of simulation, I think
would be amazing. What I worry about is that like
we will end up substituting the simulation for reality.
And maybe that's a, you know, bloodite kind of opinion, but
like, that's that to me, like feels like what I would not want
to do personally, Yeah. Yeah, and I think it's a it's a
the memorable thing I remember from just kind of going on the
replica subreddit was just someone one of the most liked
posts or upvoted was someone that had basically found a way
to hack the algorithm to be a good friend, which in this
community meant basically like downvoting times.
The the AI friend would say things that are like critical or
not positive and uploading everything basically that when
it said things that were friendly so that it would ensure
that it had a friend, virtual friend in this case, that was
always supportive, that was always super, super positive and
so on. And it kind of becomes like a
little bit of dystopian in terms of what you were describing here
in terms of a bit Huxley. And it becomes very unfortunate,
but also to tie things up even more.
So a need to have your kind of work when it really comes to
understanding how this interchange happens between
algorithms, how they're built, how they're recommending things
for us, but also how we're kind of using them in turn.
And understanding that kind of interchange is, is so important
right now. And I'm really happy that we get
a chance to explore some of it today.
I feel like we could talk much more about much of this, but
thanks so much for joining and taking the time and for all of
your great work on on this topic.
And. Thank you for your interest in
like, giving a platform to these kinds of ideas and
conversations. It's great to think about these
things together. And that's a wrap.
You've been listening to the Behavioral design podcast
brought to you by Habit Weekly and Nuanced Behavior.
Sam and Eileen tell me. This season is packed with
incredible insights about behavioral design and AI, so be
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though you might want to keep it away from your enemies.
In case you haven't noticed, I'm an AI voice.
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A special thanks to the amazing Dave Pizarro for our show music,
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Thanks again for tuning in. We'll be back soon with another
exciting conversation where behavioral design and AI
intersect. Oh.
And do you see yourself as the kind of person who watches Love
Island and enjoys watching Love Island?
And then the enjoyment while you're watching it versus the
regret you may feel later after, you know, however many seasons
of binging it? Yeah.
Are are we still talking about me as a as an example?