Hi, everyone. I'm Chris Chen from Elon University. I'm very
happy to present this paper on behalf of my co author, Dr.
Shyam Sundar. And our paper is trying to understand how
labeling quality influenced users's trust in AI. Let's first
imagine we're interacting with an emotion reader AI system,
which has been designed to classify people's facial
expressions into different categories, like happy versus
sad. And if the AI system tells you that, it has been trained on
a sample of label data like this, like, all these images are
labeled as happy. And, this group of images are labeled as
sad, How do you feel about the AI system? Well, you may take a
close look at each of the images, and you found that the
first image is pretty happy, which is good. But, the picture
right below us doesn't seem to be so happy. So the problem in
data labeling may make you question how trustworthy the AI
system is if it's trained on a sample of data like this. Well,
this may be the worst case scenario as no AI system will be
trained on the poor data set like this. But the question is
still valid. That is, to what extent people will take a close
look at the labeling quality, and factor that in when they
evaluate the training data credibility and trust in AI. So
that's the goal of our study, we're trying to understand how
labeling quality may influence user's perception of training
data credibility, and further influence their trust in AI. And
we're also interested in under what conditions this model works
by examining two moderators. That is, labeling source and
racial bias in AI performance. And we did a user study with 430
participants. And we told our participants, they're going to
interact with emotion reader AI, which has been designed to
classify facial expressions with social media images. And so far
this emotion reader AI has been trained on a large database that
has nearly 10,000 facial images. And these images were labeled by
volunteers who give tags to each of the images such as joy,
sadness, anger, fear. And so far, there are 500 people
participated in data labeling. And then, participants were
invited to see either a video, that is showing how a crowd
worker labels three example images through a video. Like in
this case, this person labeled the first image as neutral, as
showing a neutral facial expression. And for the second
image, this crowd worker also labeled it as neutral. And the
third one is a White subject image, and it was labeled as
neutral as well. Or they participate in data labeling
themselves. So if they were seeing the data labeling
opportunity, but couldn't take any action, because they were
told this feature is not quite ready for use, they're assigned
to the cue condition. If they have the chance to choose
whether to label or not, they're in the voluntary action
condition. But if they are required to label the data
before jumping to the next page, they're in the forced action
condition. And after these procedures, they were exposed to
the page, showing different levels of data labeling quality.
So if everything is labeled correctly, they're in the high
quality labeling condition. As you can see, all happy faces are
happy, all sad faces are sad. But if they're in a low quality
data labeling condition, you can see that half of the images were
misclassified. As you can see there are very obvious sad faces
in the happy category. And similarly, there are some happy
faces in the sad category. And after that, we measured user's
perception of training data credibility, that is to what
extent they think the training data of the emotion reader AI is
credible, trustworthy, reliable, and dependable. And following
this, we invited participants to see different types of AI
performance. So if there were assigned to the unbias
performance condition, all the classifications were correct, as
you can see that all four images were labeled correctly, but if
they're in the biased performance condition, the AI
systems showed clear bias against black individuals. As
you can see, their facial expressions are labeled
incorrectly, as this happy woman is classified as as sad and the
man is labeled as happy. We also have a control condition,
showing no performance to our participants. And after this
procedure, we measure their different levels of trust in AI
by focusing on three dimensions of trust, that is cognitive
trust, affective trust, and behavioral trust. And here comes
the results. So we found that showing users different levels
of labeling quality, it can influence their perception of
training data credibility. That is, if they were exposed to a
high quality labeling, they're more likely to think that
training data is credible, trustworthy and reliable, which
further increase their cognitive affective and behavioral trust
in AI. And also, we found the moderating role of racial bias
in AI performance. That is priming user the training data
is credible is likely to maintain their cognitive
trusting AI when the AI shows no performance or unbiased
performance. In other words, when the AI shows biased
performance, knowing that training data is credible
doesn't help maintain cognitive trust in AI. And this finding is
good because it tells us that priming users about training
data credibility upfront can help calibrate their cognitive
trust in AI. However, that's not the story when we focus on
affective trust or behavioral trust in AI system. We found
that regardless of the AI performance, as long as user
believes the training data is credible, they're going to trust
AI emotionally and behaviorally. And that's the trade off of this
new design. In addition, we didn't find the moderating role
of labeling source. It means that labeling source doesn't
help add values to the perceived training data credibility, as
long as the labeling quality is clearly conveyed. So we do not
encourage AI designers to incorporate this labeling task
before users interaction with the AI system. So overall, our
study provides a new design approach. That is, by showing
users a snapshot of label data indicating different levels of
labeling quality, it can encourage them to pay more
attention to training data credibility. And this early
communication approach is important because it supplement
the post hoc explanation approach in current Explainable
AI field, which focus on explanation after AI is
performance. And also, we found that this approach may work well
for supervised machine learning systems, because they have to be
trained on labelled data. And it will work very well with AI
systems that are trained on clearly defined labels such as
happy versus sad, women versus men, dogs versus cats. And also
you may benefit AI systems that are trained or visual data like
facial images. Yeah, that's all of my presentation. And thank
you for watching this video. If you have any questions or
comments, please feel free to email us and we look forward to
hearing from you and wish you a great time at CHI.