- [Connor] Hello, and welcome to my presentation today
on Having the Right Attitude:
How Attitude Impacts Trust Repair in HRI.
This was a paper I worked on
with professor Lionel P. Robert,
and both of us have the affiliations
at the University of Michigan's School of Information,
while Dr. Robert is a core faculty member
at the Robotics Institute.
So first off, why did we look at trust repair?
Trust repair is important because as some of you might know,
robots are in a place where learning
is becoming increasingly important.
This is how they're able to deal
with potentially novel tasks,
or adapt to different environments and preferences.
One of the tricky things about learning,
as any grad student will tell you,
is it involves making a lot of mistakes.
This means that robots will make mistakes,
and likely more than once.
For robots to be given the space to improve,
however, they need to maintain humans' trust in them.
They can't be abandoned before they're able to improve.
One of the methods to main a level of trust while you're,
for lack of a better word, being somewhat untrustworthy,
making mistakes, learning,
is to implement different forms of trust repairs.
Trust repair can be defined as efforts
undertaken by a trustee to restore trust
following actual or perceived trust violation.
Apologies, denials, explanations, and promises
are the four most popular approaches to this
based on the human-human literature,
and each have been looked at in the HRI literature as well.
Apologies are expressions of remorse.
Denials are a rejection of culpability
or that a violation happened.
Explanations are similar to justifications,
but they provide reasons why something happened,
so the providing of context, something like that.
Because of X, I messed up.
And promises are assertions
that convey positive intentions for the future.
So at this point, you might be asking,
do these repairs work in HRI?
Do the same strategies
of repairing trust in the human-human literature carry over?
And unsurprisingly, in HRI,
the literature is somewhat mixed.
Sometimes these strategies work.
Sometimes they don't.
In short, it's everyone's favorite phrase, it depends.
But the next rational question based on it depends
is what does it depend on?
Why are these studies producing different results?
Well, as you may have gathered from the title of this talk,
we think that it might be positive attitudes.
The next thing you might be asking, though,
is why do you think that?
And that gets a little bit more complicated.
We know that positive attitudes
influence humans' expectations of robots.
If you have a higher positive attitude,
you'll have higher expectations of the robot.
If you got a less positive attitude,
you'll have lower expectations.
The thing about expectations, though,
is that they are not always met,
and indeed, there are times
when your expectations will be shattered.
When this happens, we are uncomfortable.
Psychological theory has a name for this.
It's called dissonance.
Trust violations, in particular,
can lead to this discomfort,
i.e., this dissonance that we feel.
Now the natural thing
for humans to do when they are uncomfortable
is to make the uncomfortable situation go away.
In theory, this comes about in two ways
as far as dissonance is concerned.
We either analyze and change our expectations,
which is a little harder,
or we justify and reinforce why our expectations are right.
So what's all this got to do with trust repairs?
Well, basically different repair strategies
may be tailored to one of these different methods
of removing dissonance more so than another.
In this paper, we propose that apologies and denials
seem to encourage changing one's behavior,
while promises and explanations
seem to reinforce existing positive attitudes.
So when your expectations are violated,
you'll adopt one of four different repair strategies,
but these can be grouped mechanically
based on cognitive dissonance theory into two sort of camps,
encouraging change or reinforcing existing beliefs.
This leads us to our first hypothesis,
which is that human-robot trust repair strategies
that seek to reduce dissonance
via justification and reinforcement are more effective
for individuals who have greater positive attitudes
than trust repair strategies that do not.
While it might be easy just to leave it there,
it's important to consider
that when a robot messes up one time,
it's gonna mess up again.
It's realistic to expect
that repeat errors are going to occur.
And in the human-human literature,
we've seen that these can broadly decrease
the effectiveness of trust repairs.
Fool me once, fool me twice, that old saying.
So in some ways, we expect that attitude
won't play an important role if repeat violations happen.
This leads us to our second hypothesis,
which is that human-robot trust repair strategies
are less effective over repeated trust violations,
independent of positive attitude.
Okay, great.
We got hypotheses.
We got a general theoretical framework.
Let's talk about what we're gonna do to test this.
First off, we created a scenario where we have a task
where a human collaborates with a robot.
You can see this environment here.
Errors occurred at three equidistant time points,
and this study was conducted online.
So we had a lot of different participants.
We have a task.
The robot collaborates with the human.
Awesome.
We've got a reasonable task,
and I'm not gonna detail it here
just in the interest of time, but we've got what we need.
So with this task inside this environment,
we were interested in manipulating things,
as you do in experiments.
In particular, what we manipulated
was the trust repair strategy that was given.
This was either apologies, denials,
explanations, or promise.
We also included two conditions
used for the sake of manipulation checks.
This was a no error condition
where the robot performed perfectly,
and a no repair condition
where the robot was silent after violations.
We also measured positive attitude
as part of a pretest questionnaire.
This allows us to incorporate it into our analysis
and look at the relationship
that a positive attitude might have
on the efficacy of these different strategies.
This leads us to our dependent variable,
and the primary dependent variable
we were interested here was trust decrease,
or how bad violations were considering someone's trust.
Trust repair strategies seek to decrease the decrease,
so it's a little confusing,
but basically the less bad it was,
the more effective the trust repair strategy was.
So, we got all this methodological stuff.
We got a good study design.
Let's see what we got.
What were the results?
First off, first thing you want to do
when you're in this situation
is make sure that what you manipulated actually worked.
Given that trust was significantly lower
in the no repair condition,
this is where the robot was silent,
than in the perfect condition
where the robot didn't make any mistakes,
we realized that our violations decreased trust effectively.
In short, our manipulation check passed,
and we're good to go.
Trust was decreased.
With that out of the way,
we wanted to determine what kind of interaction effect
might be there between different trust repair strategies
and attitude over these repeated measures,
so we ran a repeated measures ANCOVA,
that's an analysis of covariance,
and we did see a significant effect.
We saw that individual attitudes
appear to significantly moderate
the effectiveness of human-robot trust repair overall.
But, we also wanted to look at this at a higher fidelity,
so we ran some post hoc tests, and in these post hoc tests,
things got a little interesting.
In particular, we saw that the impact of positive attitude
was only significant after the second violation,
and we saw that the impact of positive attitude
was different depending on the repair strategies
that were used.
In particular, this is most pronounced
when you compare the promises,
the little, blue dotted line there,
to denials, the green line.
Completely opposite directions.
And so this was something that is insightful
and provides some new perspectives
on how positive attitude might impact the relationship
or the general efficacy of trust repairs.
So from this study,
we can gather a few different insights
that are contributions to the field.
First, the influence of individual differences
was not uniform across repair strategies,
therefore it's likely that attitude impacts some strategies,
and might not impact others.
Studies that don't incorporate positive attitude, though,
may not be seeing entirely accurate results,
as this variable could be influencing
or producing non-significant
or overwhelmingly significant results.
In a similar way,
we saw that trust repair was not significantly influenced
by strategies after the first error,
but was after the second.
So it's really important
that we incorporate multiple violations into future work
because it seems that only looking at trust repairs
after one violation, i.e., this first time point,
you only get part of a bigger picture.
Our third takeaway relates to cognitive dissonance.
Cognitive dissonance could be a useful tool,
theoretically, for explaining trust repair
and categorizing the different types
of repair strategies that exist.
Currently, no framework that incorporates this is there,
but future studies may wish
to consider cognitive dissonance
when building models or theories of trust repair.
Finally, attitudes towards robots
were found to influence trust repair in HRI.
Therefore, mixed results in HRI
may be due to positive attitudes,
or even other individual differences that exist.
More research is needed along these lines.
Finally, I'd just like to say thank you
for coming to talk and for being at HRI.
Without further ado, we can see you at the session
and we'll have a live discussion of these results.