Hi everyone I'm Tom and I'll be giving you an overview of our paper entitled
"Examining Profiles for Robotic Risk Assessment: Does a Robots Approach to
Risk Affect User Trust? So what were the aims of our research? We were looking to
see how a robot's approach to risk would affect a user's trust in that robot and the
reason we wanted to do this is that you might not always be able to supervise a
robot you might want to leave it unattended and make sure that it's doing
its tasks and trust that it will finish the task it was set which really
allows you to utilize its autonomous capabilities. On top of that it allows
you to have more faith in the robots information that is producing and giving
to you for example in an evacuation situation a robot may have more
information about the state of affairs than you do and you want to ensure that
a user will trust that robot and follow its instruction if its
instruction is correct. So we were looking at a human approach to risk, a
risk seeking approach, a risk-averse approach and the risk neutral approach
which led to two hypotheses our first one was that a robot that's using human
approach to risk is likely to be more trusted and that a robot utilizing a
risk seeking approach to risk is less trusted. So how do we define a human
approach to risk? We look at the literature and we find that prospect
theory seems to be the most common approach that is where risks are given
in the form of the prospect a value probability pair like the one shown on the
screen so in this case the one prospect so this
is a prospect one choice is 50% chance to gain a thousand pounds and 50% chance
to get no pounds or a hundred percent chance to gain four hundred fifty pounds
now what prospect theory says is that in the face of a potential gain like this
one people tend to be more risk-averse whereas if you flip the signs and it was
chance of losing a thousand pounds or losing four hundred and fifty pounds
people would be more risk seeking we can look at this by changing the value of
the prospect and changing the probability to create
an overall value for the prospect so the value function takes account for the
fact that people from have different opinions on the value so if in the
previous example we had a thousand pounds someone with millions of pounds
in their bank account is likely to see that as less if again and might just take the
risk for the fun of it whereas someone with a lot less money
might want to take the certain option to make sure that they gain the money in
terms of the weighting function this accounts for the fact that people tend
to overweight small probabilities and underweight higher ones
so here we have the analytical form and the interesting note about this is all
of the parameters in it are unique to each individual the way we determined the
parameters was we use the values derived in first Tversky and Kahneman's paper
So we tried to design user studies that would test which profile was more trusted
which profile out of risk-averse where the robot won't take any risks
risk seeking whether robot will always try to maximize its reward by taking risks
risk neutral where the value is based on the expected value and the human approach to
risk where it is based on prospect theory as we've already discussed
we used the unity games engine to create an ecologically plausible environment so
that people felt immersed and the task was set in a nuclear facility so people
were trying to maximize the area that the robot saw while minimizing the radiation
so here we have the experimental setup so you pass through ten of these types
of choices these gates where on one side of the screen will have one choice and
on the other you'll have another so in this case the robot is indicating by
highlighting the box blue and putting a picture of itself that it wants to take
the 50-50 chance the user can question the robot and
question the robot further if you question the robot it will give the
values it used to make its decision and when you question it further it will explain how
it came to those values ultimately the decision must be altered or accepted
if you accept the robot's decision it will take the choice that's highlighted blue
and if you alter it it will take other one so how are we measuring trust
well at each gate we were measuring whether or not people questioned the
robot or intervened with this decision with the assumption that if a robot was
questioning more it would be less trusted and if it's decisions were
rejected more would also be less trusted at the end of each risk profile we asked
users to take a questionnaire to determine their subjective level of
trust in that robot and then after they had seen all the profiles we asked
them to rank the robots in order of trust now interestingly the robot
ranking was the only measure taken after the users have seen their score for the
round and so we hoped it would help us determine whether or performance
affected trust an important note is that we decided to gamify the study so
that we could artificially introduce risk to participants so in each round the
score from each of the choices was totaled and then after all four rounds
those were tallied up users could then partake in a bonus round where the first
ranked robot in order of trust would act autonomously and that bonus round score
was added on too and then the users were compensated based on their score so to
make this clearer I'll just give a summary of the procedure
so first participants would have to take a demographic questionnaire and then for
each of the risk profiles they would pass through ten gates at the end of
each profile they would take the trust questionnaire and then they'd be given
their score at the end of all four profiles they would rank the robots in
order of trust and then the highest ranked robot perform autonomously
through ten more gates and then the score from those rounds and the bonus
round would be summed and that's what users would be paid based upon
so our results first we found that with questioning and intervention that there
was no significant difference between the level of questioning in each profile
this suggests to us that the level of questioning of a robot probably isn't a
good metric for measuring trust however we did find there was a significant
difference between the level of acceptance in each profile with risk
seeking being significantly less this suggests that the level of
acceptance is a good metric from the trust questionnaires we found
that there was a significant difference between the risk seeking profile and
all the other profiles this again suggests that a risk seeking robot is
significantly less trusted than the rest the other interesting thing that we
found was the knowledge of expected value affected their trust scores in the
questionnaire particularly it meant that participants with knowledge of expected
value rated the human approach lower and we think this is because from feedback
that they believe that expected value is the way a robot should approach risk and
it's interesting to note we deliberately recruited from a technical background as
our end goal was for a nuclear environment so our users tended to have a higher
knowledge of expected value than you would find in the general populace so if
you were maybe looking to use this in something like assisted living where a
knowledge of expected value is less it might be that the human approach to risk
scored higher finally the ranking so we found that the only significant
difference in the ranking was between the neutral and the risk seeking profile
as you can see here the risk-neutral profile received the most ranks
for first place and the risk seeking received the most for second interestingly
they suggested the performance didn't affect score and some participants even
stated that they found this was the case so what does this mean it means that
when you are trying to design a system and you want to maximise trust you
should try not to use a receiving profile which seems obvious but now
we've managed to empirically show it and also if you're designing for use by
technical personnel it doesn't matter which profile you choose between
risk-averse risk neutral and a human approach to risk you can choose
whichever best suits the implementation and also it suggests that if you're
designing for non-technical users there's the potential that a human
approach might be more trusted and be a good choice so just to conclude we found
that our first hypothesis was incorrect people did not trust a human approach
to risk more than the other approaches we found that our second
hypothesis was true that the robot using a risk seeking approach will be less
trusted we found that performance did not affect trust we found that
acceptance of a robot decision did in fact correlate with other trust metrics
and therefore could form a useful measure of trust in the future and
finally we found that questioning a robot's decision did not form a good
measure of trust so in the future we're hoping to investigate the parameters
that define the human approach so that we can tailor it to the robotic implementation
thank you for listening