Hello, I’m Anastasia Kuzminykh, and this is a video presentation of work
published at CHI 2020
that we've done with my colleagues at the University of Waterloo, Canada.
This work is focusing on how people anthropomorphize
conversational agents, and why this matters to us.
The general purpose voice assistants,
such as Alexa, Google Assistant, or Siri
are becoming more and more popular
so more and more users are interacting with them on a daily basis.
And these systems, in essence, rely on users’
voice interactions with a persona (an agent)
who is deliberately designed to speak and act human-like.
So because of this, voice assistants end up being integrated
not only in the technological space of a user’s life
(the ecosystem of devices in their environment),
but also in their social space (with whom users communicate)
And thus, these systems are often being anthropomorphized –
which means that people start attributing lifelike qualities
and traits to these agents.
Anthropomorphization heavily defines the interaction style
that people adopt with these systems,
and affects users’ expectations, trust, and their overall satisfaction.
And so far, research has actively explored
the degree of anthropomorphization of voice agents -
whether it happens or not,
do we see these systems as human-like or
do we see them as machine-like.
However, very little is yet known on what happens when
anthropomorphization does occur - are all the agents then the same?
Well, intuitively, probably no.
But if not, is their perception unique for every user
or can we find some underlying patterns there?
And this is exactly what we wanted to explore in this study -
what are the mechanisms of anthropomorphized
perceptions of voice assistants.
So what did we know about these mechanisms from before?
First of all, mostly in robotics, people differentiate between
designing agents behaviors, such as
their emotional responsiveness, how warm they are in the interaction,
their personality, and so on,
and designing agent’s physical (or visual) aspects,
for example, their facial and body expressions.
Say, Roomba vacuum has only behavioural traits,
but wax sculptures have only visual ones.
And by design, voice assistants are capable of displaying
mainly behavioral anthropomorphic aspects.
But what personality and behavioural aspects matter to users?
Is there any structure to them?
And we hypothesized that, if there is any structure,
than these perceptions will be different,
and consistently different for different agents.
We also hypothesized that the way people perceive
the personality and behaviour of an agent
would affect how they visualize them -
so how users think the agents would look like, if they were humans.
Especially since an agent’s answers might include visual references,
for example, Siri’s "I’d blush if I could".
So then, if there are consistent patterns in behavioral perceptions,
we can also expect consistent differences in visualized perceptions.
And in the rest of my presentation, I’m going to talk about
how we explored these hypotheses and what we found there.
First, how - so what methods we used.
We took 3 agents - Alexa, Google home, and Siri,
and we first asked participants to interact with them,
because we wanted to allow for in-situ comparisons of three agents
so that later we were able to tease out
the differences as they were seen by participants.
The interaction session was followed by semi-structured interviews
where we asked participants what each of the agents
would be like if they were humans,
and what their definitive characteristics would be.
In the next phase – a visualization exercise – participants were asked
to create an approximate visual representation
of Alexa, Google Assistant, and Siri.
And here it was actually a tough methodological call:
what to use as medium - some standardized platform or free-form drawing.
And eventually we decided to go with using predefined software.
We used an open source web avatar generator
And we did that because, first of all, we wanted to overcome
potential resistance from participants to a hand drawing exercise,
and we also wanted to allow better comparison
between visualizations between participants,
regardless of their drawing skills.
And we concluded with a debriefing interview to capture
some general notes and comments from participants
about their experience.
And let’s first look at the descriptions of agents’
character and behavioral specifics.
And the main highlights here are that
the perceptions of an agent’s character are indeed structured,
and they are structured according to five categories:
approachability of an agent, sentiment toward a user, their professionalism,
intelligence, and individuality.
And furthermore, they are indeed different for different agents.
So each of the identified five major themes
actually represents a comparative scale for an agent’s character.
And the first scale - the theme of approachability -
reflects how emotionally distant or approachable an agent is.
The "distance" end of this scale includes, for example, an agent being
"snarky" or "closed off"... And it often would be in contrast to
the user’s willingness to engage in closer and warmer relationship.
For example: "very one-sided relationship... like they didn’t want
to talk to me, like "here’s your answer and go away""
The "approachable" side of the scale reflects
perceptions of an agent as emotionally warm,
friendly, and willing to interact with a user. For example,
being "welcoming", "feeling more like a friend", being "outgoing" and "chill".
The second theme – an agent’s perceived sentiment toward a user –
is defined by a dichotomy from genuine to disingenuous agent’s attitude,
so how trustworthy and how focused they are on a user.
And the "genuine" aspect here includes descriptions
of an agent being benevolent, caring,
altruistic... For example, "coming from a good place",
being "patient", "accommodating", "thoughtful",
or "really trying to help you".
Opposing these caring, benevolent perceptions,
the "disingenuous" side here
reflects assessments of an agent as cunning, indifferent, or inconsiderate.
For example, descriptions such as "sly", or "ingenuine", or "pretending",
or about Siri: "uses her sass as a defense mechanism".
Professionalism mostly refers to the agent’s communication style
as in work settings,
for example, "textbook assistant, no-strings attached relationship".
And there is no defined negative value here, but we did find that
comparisons between agents often reflect the differences in
the degree of professionalism.
The intelligence of an agent was mainly discussed as a positive value.
For example, descriptions like "witty with knowledge", or "well-read", or
"intelligent and sophisticated".
But rarely there would be descriptions
of agent’s negative intelligence,
for example, "a little on a dumber side".
The last theme - the agent’s individuality - refers
to participants’ perceptions of how much character
an agent is displaying and the specifics of this character.
So this theme is represented by a dichotomy
of neutral versus defined individuality.
And the neutral aspect here includes descriptions that explicitly highlight
the ordinariness of an agent’s character,
for example, "shy...not many interests",
or "It’s just some random kid trying to blend in".
The specifics of defined individuality would differ for each agent,
for example, Alexa would have "a glowing personality",
and Google would be "warmer but nerdy... nerdy chill".
So the behavioural perceptions are both structured
along these five dimensions
and are different for different agents.
But what about the visualised perceptions?
So we analyzed both how people were verbally
describing the appearances of the agents
and the outcomes of the visualization exercise.
The visualization exercise outcomes were first composed into collages.
And we also composed a cumulative visualization
for each agent by overlaying avatars.
As you can see, the outcomes are quite illustrative
on the differences between the agents.
And in the descriptions and in the avatars,
we have identified five main parameters
that would differ. And let me give you some examples here.
The build was mainly referring to the height.
For example, “Alexa slightly taller because daunting."
However, there was no consistent trend here.
With age, thought, there was a trend -
Alexa is generally perceived to be older,
while Siri is perceived to be younger.
For example, "Siri sounds younger, like late 20s.
Alexa, I think, like 40s. Google sounds older... so between the two, mid 30s?"
The specifics of facial expression often reflect
the perceived approachability of an agent,
for example, "slight smile, because she is more playful".
This is also reflected in the described differences in the agents’
facial expressions. For example, "Google just seems stressed,
they’ve got a lot of stuff. Seems a little like their mind is
somewhere else if you are having a conversation with them.
Alexa is just maybe a little bit smiling,
and Siri would be smiling a lot and has a lot of energy."
The clothing style was heavily dependent on how defined
was the personality of an agent for a particular participant.
For Siri, such clothing descriptions revolved around
daring personality with highlighted femininity:
she would be wearing "heels, mini skirt, crop tops", or, for example,
"I almost picture Siri in like a school-girl outfit.
Like a pleated skirt... like any teenage romcom"
Google’s clothes in this category are associated mainly
with a stereotypically "awkward" persona:
"They could be wearing jeans and t-shirt but it doesn’t really fit them well",
and "Google would dress like... glasses and the weird skirts and leotards".
Alexa’s clothes in this category often
reflect her caring and cozy nature.
For example, she would be "wearing mom jeans. And running shoes,
because that’s a very mom thing. And glasses".
So with that, we have identified the main themes
along which both behavioural and visual perceptions
of conversational agents are structured.
But now, we wanted to see what design elements
affect each of those perceptions.
And we identified four categories of the designed features
mentioned by our participants when they
were explaning their perceptions.
The first category here was the category of provided information,
which includes the amount of information and level of details. For example,
"they give more info and so it seems like they care
about you understanding what they’re saying".
It also includes the very fact of providing the information.
For example, "Siri is just cold, she copped out on a lot of answers
and that made her more distant".
Second, the designed interaction style of an agent,
such as responsiveness to prompts,
prompting users back, for example, "seems closed off,
because doesn’t do any prompts back like ‘how are you’ and stuff",
and the tendency to use jokes for example, "when I asked
what’s her favourite colour, she told me a joke.
So yes, way more playful".
The third category is agent’s voice characteristics,
for example, "More enthusiastic? like there’s more inflection
in her voice, sounds a bit more fun".
And finally, we included here the category of associations
motivated by the perception of the company,
for example, "Alexa is more chill. Maybe because a brand
reminds me of books and stuff".
And in the paper, we demonstrate how all these parameters
have different impacts on the different behavioral dimensions
and the visualized perceptions of the agents.
So to conclude, in this study we found that both
anthropomorphized behavioural perceptions
and visualized conceptions of conversational agents,
first, have some structure to them,
second, have consistent differences between agents,
and third, are interconnected.
And with that I would like to thank you for listening
and invite you to read the full paper
for more details on this study.
And if you have any questions please do not hesitate
to contact me using the email on this slide (akuzminykh@uwaterloo.ca).