Hi, this is Eric Kim.
And today I'd like to discuss our paper: Designing Chatbots with Black Americans
with Chronic Conditions: Overcoming Challenges against COVID-19.
This paper was written by me, Jana
and my two advisors, Lionel Robert and Sun Young Park.
Although chatbots are being actively deployed within the health care
domain due to their advantages of being user friendly and easy to use,
they can become problematic when they do not consider the cultural
context of a particular population, especially marginalized groups.
Lack of incorporating the cultural context
can lead to reduced engagement due to lack of trust and rapport.
So how can we design chatbots that address
the cultural context of marginalized communities?
We wanted to answer this question by specifically focusing
on Black Americans with chronic conditions in the U.S.
The reason for this is that this population has suffered
from a disproportionate impact from COVID-19.
Also, chatbots have the potential to meet the needs of this population
through increasing healthcare accessibility, disseminating health
information to those with low health literacy
and increasing trust towards health information.
Based on this research question,
we conducted a study to make the following contributions to the HCI community.
We analyzed and defined the roles, features and challenges of chatbots
for the Black American community in the context of COVID-19,
which had not yet been clearly addressed in the HCI literature.
We suggested design guidelines for chatbots
aiming to support the health of the population.
And finally, we allowed participants to design their own chatbots
remotely through Zoom for deeper engagement in the design process.
We recruited 18 Black Americans with chronic conditions
in the Midwest region and implemented our study through Zoom.
Participants were ages
21 to 63 with the mean age, 47.
They consisted of 16 women and two men.
Many of their chronic conditions were related to high blood pressure,
although we did have some asthma patients.
We went through three steps in our study.
First, participants talked to a Facebook chatbot designed by the researchers.
The purpose of this activity was to allow participants to get acquainted
with chatbots and gather demographic information from the participants.
Because many of our participants had no prior experience
with chatbots, this process was necessary.
Second, we had semi-structured interviews with our participants.
We asked them about their challenges in maintaining their health
during COVID-19 and how they obtained information about COVID-19.
This helped us understand their health behaviors and barriers,
which influenced how they designed their chatbots.
The final step was to actually design their chatbots
using a platform called botframe.com.
We gave them six scenarios of possible challenges they faced during COVID-19
and had them choose one that they can best empathize with.
Based on this scenario, they
designed a chatbot that could help them go through the chosen challenge.
After the design activity, we asked them to explain
their expectations and reflect on their design decisions.
We recorded and transcribed the whole process,
which was about 27 hours of recording.
Based on the data we collected, we went through thematic
analysis and affinityt diagrams to identify emergent themes.
The four researchers gathered together in weekly meetings
to review and discuss those themes and identified higher-level themes.
Some examples of our resulting codes were chatbot as a communication channel,
adapting to the user or personal health impediments of users.
From this analysis, we identified three main findings.
Roles of the chatbot, features
of the chatbot and challenges and barriers of using the chatbot.
Due to time constraints,
I'll briefly introduce the first and the second one in this video.
Please feel free to read our paper for more information.
First, the role of the chatbot.
We identified four roles the participants expected
from the chatbot: mediator in accessing health services,
personal assistant that provides personalized health recommendations,
information hub for updates on COVID-19 and its vaccines,
and trustworthy voice of the community.
Regarding the mediator in accessing health services, participants
expected the chatbot to act as a bridge between the user and health experts.
This reflected their own experience as a chronic condition patient
who had to look out for themselves during the pandemic.
For instance, P1 designed a chatbot that served as a bridge
where the questions user asked the chatbot could be delivered
to relevant health experts and receive credible responses.
Moreover,
participants wanted a support system for urgent needs
without having to wait a long time to reach their clinicians.
That's why P8 wanted the chatbot
to connect between individual clinicians and patients.
She wanted the chatbot to be a temporary clinician that users can speak to
because users should have access to healthcare at any time.
Participants also wanted the chatbot
to be a trustworthy voice of the community.
During the pandemic, participants felt that their voices were not represented
and have been deprioritized in favor of other needs,
even when they were being disproportionately impacted by COVID-19.
Therefore, P7 wanted the chatbot
to be a channel that listens to the voice of the community
and becomes a virtual representation of Black American leaders.
Let me move on to features of the chatbot.
By features, I mean, characteristics or capabilities.
Participants wanted from the chatbot a persona that engendered
trust and comfort.
While designing their chatbots, participants
seemed to first draw a mental model of what the chatbot is
and then consider the conversation style and its capabilities.
P7, for example, said that people would be able
to trust the chatbot more if it looked like a medical professional
and that would end up following its advice.
It was also important for the chatbot to produce comfort and be relatable,
as you can see from how P5 wanted
to give the chatbot the persona of his mother.
He projected his image of his mother onto the chatbot because he believed that
the chatbot could also act like his mother in her stead, when she was not there.
So what do these findings tell us?
One important takeaway is that our Black American participants
wanted the information sources of the chatbot to be transparent.
Because of the inherent distrust towards
the medical system of the Black Americans in the U.S., they emphasized
that they should pull this information from reliable and trustworthy sources,
and those sources should be clearly stated from the chatbot.
Another takeaway is that the chatbot design
should incorporate the local community.
Interestingly, when participants designed their chatbots,
they immediately thought of the needs of others within the community,
such as their family, friends or neighbors.
Because they share the same historical and cultural background.
They wanted the chatbot to serve the group's needs and overcome
the group's challenges and thought of ways to help out the community.
This tells us that when designing chatbots for our population,
it is important to understand the needs of local communities, not only individuals.
The last takeaway
is to consider the socioeconomic factors of the population.
Participants often designed
a chatbot that can support the needs of lower-income households.
Their designs worked to fill the information gap
or increase medical accessibility through the chatbot.
We believe that it is important to design such chatbots
so that the needs of the underserved population can be met.
Thank you so much for listening, and let me know if you have any questions.