Hi, I'm Pranav, and today, I'm presenting our work titled,
Conceptual Metaphors Impact Perceptions
of Human-AI Collaboration.
As conversational agents have become pervasive,
it is important to understand the mechanisms that
influence users' experiences of these agents.
What causes some AI agents to get adopted,
while others are discarded?
Why would Xiaoice amass millions of monthly users,
while the same techniques following Tay
like to be [INAUDIBLE] for eliciting
anti-social social interactions?
Experience of a system are typically
understood as being mediated by a user's
mental model of that system.
And developmental models can lead
to more informed and satisfying interactions.
But how do we explain why Tay and Xiaoice is so very
different reactions, despite having similar learning
technology?
This suggests we need additional theoretical scaffolding
to understand this phenomenon.
An important yet relatively unexamined
aspect is how these systems are introduced
to people, particularly the metaphors
that designers attach to these systems.
AI systems today are usually accompanied
by conceptual metaphors, like Tay, Microsoft's AI fan,
from the internet that's got zero chill, or Woebot,
your charming robot friend, or a sympathetic ear, Xiaoice.
The formula is in motion, conceptual metaphors
convey complex ideas using simple terms.
And designers often rely on these metaphors
to implicitly communicate functionalities of the system
and to influence user expectations.
Across our studies, we seek to understand
how metaphors used to describe AI systems affect
user evaluations and user attributes.
To characterize the space of metaphors,
we draw on social cognition theories
around impression formation.
The Stereotype Content Model tells us
that judgments along the dimensions of warmth
and competence are the prime factors
in impression formation.
One is characterized by notions of sincerity
and good-naturedness, while competence
is characterized by notions of intelligence and skillfullness.
For example, we will judge a baby
to be high warmth, but little competence,
and a professional to be high warmth and high competence.
You would expect that impressions
of computing systems to be dictated by the same factors.
As the Media Equation suggests, we
apply social rules to our interactions with computers
as well.
Our first study explores how the warmth and competence projected
by the metaphor affects user evaluations and user attitudes.
In this study, participants are used to a conversationally AI
system using a metaphor.
And this metaphor varies depending on the study
condition they are assigned to.
After this, they computer task using the system,
and we measure their attitudes and evaluation.
The only thing that changes across study conditions
is the metaphor displayed.
The underlying system remains the same.
Participants in the study were asked
to complete a travel planning task
with a Wizard-of-Oz conversational agent.
To curate a set of metaphors, we listened several candidate
metaphors and asked another batch
of crowd workers questions about these metaphors.
These responses were used to drive the warmth and competence
levels projected by the metaphors.
This allowed us to locate where these metaphors lie
in the warmth and competence space, which
has four quadrants.
High warmth-low competence, high warmth-high competence,
low warmth-low competence, and low warmth-high competence.
For the first study, we select one metaphor of a quadrant,
giving us four study conditions.
Along with the control condition of participants
that show no metaphor, these from the five
conditions in study one.
After completing their task with the system,
participants were polled for their desire
to cooperate with the system and for their intention
to adopt the system.
We find that participants express a stronger desire
to cooperate with the agents that projected lower competence
and agents that projected higher warmth.
We find that participants are more
likely to adopt an agent that projects low competence,
and they are less forgiving of mistakes
made by AI systems that project high competence.
We do not observe the effects of warmth and their adoption
intentions.
These results suggest that it's better
to lower initial user expectations by projecting
low competence and to then positively violate
those expectations.
Interestingly, we find that the relationship
between competence and adoption intentions is non-linear.
And more extreme violations of expectation
lead to stronger effects.
While we've seen the benefits of projecting low competence,
it is also possible that low competence metaphors
can drive away potential users before they even
trial the system.
Through a survey, we found that participants
were more likely to try out AI systems that
were described as high competence and high warmth.
So it becomes clear that associating a high warmth
metaphor is always beneficial.
But the choice of competence level
becomes a more nuanced decision.
One possible approach might be to choose a high competence
metaphor with lower competence expectations
right after interaction begins.
Our findings provide a new lens to interpret user behaviors
around existing AI systems.
We take metaphors attached to existing systems
and use the same method as before
to gather judgments of warmth and competence.
We project these existing systems
into the warmth and competence space
and find that all systems project high competence, which
might explain why people are constantly
dissatisfied with them.
We find that Tay projects the lowest warmth, which
might explain why it elicited the anti-social interactions
that it did.
You can read more about our study
and our findings in our pa--