Hello, everyone.
My name is Chun-Wei Chiang, Ph.D.And I'm a PhD student at Purdue University.
I work with Zhroran Lu, Zhuoyan Li
, and my advisor, Dr. Ming Yin, for this work.
Today, I will present our work on behalf of my coauthors.
Have you ever heard of the old saying that "two heads are better than one?"
This old saying highlights the value
of collaborations and the power of collective thinking.
Indeed,
discussions among a group of people can often bring together diverse perspective,
ideas, and experience to make better decisions.
But are two heads still better than one in AI assisted decision making settings?
Today,
I would like to share our finding about the difference in the behavior
and the performance of groups and the individuals in human AI collaboration.
Every day, we are faced with decisions
that can significantly impact our lives and those around us.
Artificial intelligence is increasingly
being used to assist people in making decisions in various domains,
ranging from finance, healthcare, education, and criminal justice.
And in this AI-assisted decision making settings,
AI models often make predictions
while people can decide whether to follow or overturn the recommended decisions.
Previous research has explored
the effectiveness of the collaborations between humans and AI in decision making
from multiple aspects such as decision accuracy,
humans' reliance on AI, fairness in decision making,
decision confidence, accountability, and humans' understanding of AI.
Most of the existing research
focuses on how individuals collaborate with AI in decision making.
However, in reality,
many decisions are made by a group of people instead of individuals.
Using criminal justice as an example, many countries have adopted the jury systems.
A jury is a group of people selected to hear evidence and arguments
presented in a court case and then decide on a verdict.
Since group decisions making is often not
a simple aggregations of individual's decisions,
it is still unclear whether the finding for individual AI collaboration can be
generalized to help groups interact with AI assistants.
Therefore, our research aims to explore how groups and individuals differ in their
performance and behavior in AI assisted decision making.
We evaluate the difference from the six aspects we mentioned previously.
To answer these questions, we designed and conducted a sequence
of human subject experiment in the domain of recidivism risk assessment.
In each task, we gave participants the defendant
information, such as their race, gender, and criminal history.
Then we ask our participants to predict whether a given defendant
would reoffend within two years with the help of the COMPAS model.
COMPAS is a risk assessment tool used
in the criminal justice systems to predict the likelihood of defendant reoffending.
It was found that COMPAS is biased against certain groups
such as people of color, and it can lead to unfair and discriminatory outcomes.
By adopting COMPAS as the AI assistant
in our experiment, we could better understand how humans use
bias AI assistants in a group versus individually.
In our experiment, we had two treatments.
The first treatment is individual AI treatments.
In particular, in this treatment, we asked participants to make
the recidivism prediction based on the defendant information
and the COMAPS predictions without any input from others.
The second treatment is group AI treatment.
For this treatment, we provided a real time chat room for the participants.
The participants had to discuss with other
team members and reach a consensus on the final recidivism predictions.
In our experiment, we assigned at most three members to one group.
Our experiment could be separated into four parts.
The first part aims to understand
participants own cognitive style in making recidivism risk predictions.
In particular, in this study,
a participant's cognitive style was characterized by how their predictions are
influenced by the defendant's race and charge degree.
On this task, we did not provide recidivism risk
prediction made by COMPAS to the participants.
And we also did not reveal the ground truth answer to participants in terms
of whether the defendant actually reoffended with two years or not.
The next three parts happens on a few days after the first part.
The second part aims to have
the participants try out COMPAS and learn about its strengths and the weakness.
Hence, we provided the COMPAS predictions to participants for them to judge
the recidivism risk before they made their decision.
After they made the decision, we also displayed the ground truth
to participants so that they can tell when COMPAS was likely to be right or wrong.
In the formal test session, we randomly assigned the participants
into two treatments, and we recorded their final prediction
in each test to help us evaluate their decision accuracy,
their decision on the AI recommendation in terms of whether their final prediction
agreed with AI, and the fairness of their prediction.
At the end of each test,
we asked the participants about their competence in the final prediction.
After complete the formal test, in the next survey,
we ask participants additional questions to evaluate their understanding
of the COMPAS model and how they would assign the credit
of making correct predictions or responsibility of making the wrong
predictions among themselves, AI, and then possible their teammates.
Based on the data we collected from our experiment,
we found that there are no significant difference in the decision accuracy
and the understanding of AI between the two treatments.
In today's talk, we focus on discussing
the difference we found between individuals and groups in their reliance
on AI, decision fairness, and decision confidence.
First,
we found that the participants who made decisions in groups relied on the AI model
more than the participants who made decisions individually.
In fact, this is true, regardless of the correctness of the AI prediction.
In terms of decision fairness, we found that the individual predictions
on black defendants appears to have much lower accuracies than on white defendants.
But the prediction from participants who made decisions in groups
have a similar accuracy on black and white defendant.
In other words, group decision makers provide fairer
decision accuracy than individual decision makers.
In terms of decision confidence,
we noticed that when group decision makers challenged the incorrect AI
prediction, they would be more confident than individual decision makers.
Our experiment also enable us to identify
a few interesting patterns in how the presence of AI model impact group
dynamic in AI assisted group decision making.
We found that participants in group sometimes start their conversations
with others by quoting the AI model's predictions.
They will also use AI predictions
to support their viewpoint in order to convince other group members.
When the group could not make consensus,
participants may also use AI predictions as the tiebreaker.
But sometimes group takes the AI
prediction into account in a more critical way.
For example, sometimes the discussion among participants focusing on trying
to justify the AI prediction or to try to argue for why the AI prediction could
be wrong if they have a strong counter argument.
Moreover,
participants would even explicitly analyze the trustworthiness of the AI model
to decide whether they should follow the AI prediction or not.
To summarize, here are some key findings from our experiment.
In general, our experiment and results
highlight that people in groups interact with AI assitants
in a different way than how individuals interact with AI assitants.
Our paper contains more results about the difference between groups AI
and individual AI from accountability aspects.
Moreover, we also explore how group diversity influence group AI interaction.
Thank you again.
If you have any question, feel free to ask.