hi everyone my name is Magdalena I'm a
postdoc at the resource center for
trustworthy data science and Security in
Germany and today I'm going to present
the results of both scoping review in
which we review previous studies on user
stress calibration when interacting with
automated systems
stuff and what is calibrated Trust
it basically refers to how well
users trust matches the true
capabilities of the automation
in other words we speak of calibrated
trust when users trust a system just to
the right amount not too little and not
too much
in an own research we were curious about
how would we reach this level of
calibrated Trust which is why we thought
it would be a great idea to invest some
time and effort to get an in an overview
of how this has been done in the past
and so the idea of this review was born
how did we go about this well in the
first step we defined
which studies to include and first off
we start decided that it should only
include peer-reviewed and published
studies so you will not you won't find
preprints or theses
um second we only included empirical
Works which included human subjects this
meant that all theoretical works or
simulation studies were also included
excluded
and third this is a bit more complex we
only included studies which had true
trust calibrations
what do we mean by that it basically
says that three elements had to be
present in the paper first off users
trust had to be assessed somehow through
some measure
second the system capabilities had to be
defined but ideally manipulated
and third these two information had to
be matched at some point
so if you if we had a study for example
that just intended to increase user
stress levels
without controlling whether this level
of trust was appropriate those studies
were removed
next in the identification
stage this happened over two waves one
in August one in November and our
original search encompassed just a
Google Scholar search and Conference
proceedings
from the kai conference
cscw and the fact conference which was
then later extended to the whole ACM
library and the scopus database search
once a corpus of roughly 1000 papers was
identified three independent coders
checked for the inclusion criteria which
I just introduced
and in the end we landed at a final
paper count of 69 papers which were
included
and we really try to make this process
as transparent as possible so you can
find all papers all 1000 papers online
and supplementary material and their
justification for inclusion and
exclusion as well
just scan the QR code of this slide
okay so we arrived at r96 papers but
what were we actually interested in a
lot of things first off we looked at the
different samples of the different
studies so who are the people
um particip who were the participants
was it an online lab study with experts
or students how would they recruit it
um we had a closer look at the different
tasks of the studies which domain that
they fall into but particularly also
what was the role of the automation
within that task what were the
capabilities of that automation was it
rather rule-based or was it a deep
learning algorithm
um participants required to be experts
and of course the risk of the different
tasks
then very very Central
um was also the calibration intervention
we had a closer look at the different
study design choices and how they
affected might affect the results
and we looked at the different system
capabilities and how they were
manipulated and as well as how people's
trust was assessed and then of course
how did the authors Define if there was
a calibration or not
um we then looked at the results of the
intervention did it work or didn't
didn't it work what worked best what was
it more difficult and then finally we
came up with different calibration
dimension
so um and for the purpose of this video
um I will focus on the different
strategies to calibrate users trust some
of the results of these interventions
and as a result of these observations
the calibration Dimensions which we
developed
first the different strategies
um which were used to calibrate user
struts trust which we sorted by the time
of the intervention so for example we
found that many researchers try to
inform users before the interaction with
the system about the system's
capabilities
um some research tried included a
training phase to get users used to what
the system can do and cannot do but we
also found a lot of interventions during
the interactions through different cues
which could be alarms or warning signals
confidence cues or updates but also
uncertainty displays a lot of
researchers try to use explanations
and then of course many tried to do to
calibrate users trust also after the
interaction by providing users feedback
about their performance with the system
or feedback about the performance of the
system itself
so what were the effects of these
different trust calibration
interventions
first off actually a lot of papers
reported how users calibrated their
trust when they were not supported by
anything
and the authors could show that users
are actually aware of different
trustworthiness level
but they don't necessarily always adapt
to those left levels appropriately
and so that natural trust adaption can
be supported through these trust
calibrations and they did increase
actually the the accuracy accuracy level
of the trust calibration
but it oftentimes also can come with a
detriment of increased workload for the
users and
um as well as users just going into
complete over trust into a system
um we picked up one example of these
results
um for the explanations case so we um we
saw that a lot of explanations helped
users
um to calibrate their trust but the
effectiveness of these explanations were
depend were dependent on the for example
the task difficulty or the system
reliability for example if systems were
super reliable the explanation actually
didn't have much of an impact which
intuitively would make sense as well
and we saw a lot of different individual
differences for example people with a
higher needs recognition they responded
better to explanations
it also a lot of the results depended a
lot on the expectations of the different
users their personality their expertise
but also on the system side so was the
system anthropomorphic or not and what
was the General risk involved when
dealing with that system
and based on all of these results and
these observations
we develop four different test trust
calibration dimensions
which we hope will help
researchers to think about and develop
future trust calibration strategies
so let's finish off by having a quick
look at what we identified here
so the First Dimension is the EXO or
Endo calibration with axocalibrations
being calibrations that are employed
outside of the interaction with the
system for example the training
um user training or prior information or
performance feedback on how user
interacted with the system whereas
endocalibrations only happen during the
interaction which is what we saw most of
the time as cues or uncertainty displays
the the all of the explanations happen
during the actual
interaction with the system
seconds we identified warranted and
unwarranted trust calibrations with
warranted trust calibrations actually
increasing the knowledge which is
related to the system's capabilities
for example information about the
reliability whereas unwarranted trust
calibrations increase the knowledge
which was unrelated to the system
capabilities such as the reputation a
reputation can be really good but the
system is actually not that great but
also anthropomorphism because if
something is anthropomorphic or not
that's not necessarily reflect in Better
or Worse capabilities
third we found interventions could be
sorted into static interventions which
treat every user in every situations
equally always the same does not change
whereas adaptive interventions react to
the current level of users trust so if
users trust increases the the support
would be less and if users show too
little trust the system would react to
that by increasing for example support
cues
and fourth we've found that many
intervention focused explicitly on the
system abilities
showing users what the system can and
cannot do
whereas on the other hand some
interventions were more implicit
for focusing more on the process by
trying to making by trying to make the
systems in our workings more transparent
that would be for example a lot of the
explanation examples would fall into
that category
and due to time constraints we're not I
cannot talk more about these but if
you're interested I encourage you to
have a look at our paper
if you have any questions please reach
out through the channels in the slide
otherwise we'd like to thank you for
your attention