Artificial intelligence plays an increasingly prominent role
in our everyday lives.
Healthcare is one of the high stakes domains that is rapidly
adopting automated approaches with the development of healthcare
AI. Prior work found that people trusted human doctors more
than health care AI and were resistant to adopt healthcare
AI. But who feels this way?
And why will people with different levels of trust or mistrust
in doctors feel differently?
If so, how can we earn the trust of those who have been hurt
by these systems?
Trust in medical and technological systems is often informed
by their perceived potential to be fair or unfair.
Many social groups currently and historically experience
discrimination in medicine and in their use of technology.
What participants previous experiences influence their perception
of healthcare AI, accounting for existing attitudes towards
medical systems such as trust or mistrust, is part of answering
this question.
Medical mistrust refers to a lack of trust in medical institutions,
doctors, and health care workers.
This can be caused by previous experiences with medical discrimination,
a reality that communities of color know all too well.
Communities of color have vastly different and situated reasons
for attitudes towards particular systems.
Much of the research on medical mistrust is currently specific
to black communities, so we sought out a balanced sample
of black and white participants centering the voices of our
participants, and understanding their concerns was of the
utmost importance to us.
So we conducted a twopart study, an online experiment, and
a followup interview, to better capture how they perceived
healthcare AI.
We had to answer this question with realistic depiction of
healthcare AI.
With the help of one of our dermatologist colleagues, we
learned that Photo imaging was one of the most popular techniques
in healthcare AI and that dermatologists were already adapting
so would be technologies in their practice.
So we situated our study with scenarios informed by Longoni
at all and health care I currently used in dermatology.
In addition to asking for demographic information, we deployed
the widely used 12 item Groupbased Medical Mistrust Scale
to measure participants level of mistrust in medical systems.
We regard trust is something to be earned on the terms of
those who have been hurt and seek to better understand those
terms in their words.
So we followed up our survey with interviews asking participants
for their suggestions and reactions to healthcare eye descriptions
that attempted to address fairness and earn trust.
Our online experiment and interview results suggest that
participants who mistrust human medical providers perceive
healthcare AI as equally untrustworthy and as unfair as human
medical providers.
Conversely, people with lower medical mistrust tended to
trust human providers more and view them as significantly
more fair, especially when compared to healthcare AI.
The use of the Mistrust scale allows us to account for varying
experiences rather than only using race as a single demographic
category. Not all black participants reported experiencing
high medical mistrust, but those who did have significantly
higher mistrust than white participants in our interviews,
we found that attempts to explicitly describe a healthcare
AI product as committed to antidiscrimination was more well
received than one committed to fairness, while some were
assured by commitments to anti discrimination.
In theory, practical evidence and data of antiracist aims
were required for participants with higher levels of mistrust.
Individuals with these concerns often asked directly about
how representative the data set and training materials were
for people with darker skin tones.
Many studies use human decision making as a benchmark to
measure people's perceptions of AI, and their findings indicate
that people trust AI decisions significantly less than human
decisions, particularly for decisions that are subjective
or seem to require human capabilities.
Our work points had a critical gap in prior work.
It does not reflect important differences in social groups.
While most of the existing studies do not report demographic
information about their participants, all studies were conducted
on MTurk, whose users are predominantly white.
Additionally, our findings suggest that previous works approach
of increasing the role of human decision makers in AI systems
to increase trust may be ineffective for community who face
marginalization from human decision makers.