Hi, thank you for your interest in our research.
My name is Lisa.
I am a PhD-Student at Bielefeld University.
Today I will talk about the question: Why do we follow robots?
We conducted an online Experimental Investigation of Conformity with Robot, Human, and Hybrid
Majorities.
First of all – what does conformity mean?
Conformity has been first shown in an Experiment by Solomon Asch in 1951: Subjects tend to
give the wrong answer, when a majority of confederates have previously given the wrong
answer as well.
This is an example for conformity: we adjust our answer to the answer of the majority.
Why does this happen?
There are two types of social processes, which explain conformity:
The first reason is normative influence: We feel pressured by the majority, so we conform
with the expectations of another.
Another reason can be informational influence: We feel insecure about our answer and seek
for the truth, the majority seems to be a reliable source so we accept information obtained
from another as evidence about reality.
As robots become interaction partners more frequently, especially in the workplace, the
question arises: Do we also conform to robots?
A number of studies have examined conformity with robots.
The findings are mixed.
Some studies found no conformity effect for robots.
On the other hand, several studies found conformity effects.
Ullrich et al found a conformity effect, when the majority consisted of multiple humans
and one robot: It was a hybrid majority.
Hertz et al found that conformity depends on number of robots and the specific task,
which was conducted in the experiment.
Based on these findings, we derive the following hypotheses:
Hypothesis 1: Conformity rates will be higher in the human condition than in the robotic
condition.
Hypothesis 2: The highest conformity rate is found in the hybrid condition.
Hypothesis 3: The reasons for conformity differ across the three conditions.
Hypothesis 3a: In the human condition, conformity is influenced by normative conformity but
not by informational conformity.
Hypothesis 3b: In the robotic condition, conformity is influenced by informational conformity
but not by normative conformity.
Hypothesis 3c: In the hybrid condition, conformity is affected by both types of conformity.
We conducted an online experiment.
In our experiment, the task was to compare two applicants for a job as a long-distance
pilot and to select the more suitable person.
Subjects were told that besides them, other humans and robots perform the task
as well.
After the decision was made, the confederates’ answers were displayed.
The subjects now had the option of changing their answer or staying with their answer.
The figure on the right is an example of the Hybrid condition: The majority consists of
robots as well as humans.
One experimental trial looked like this: Subjects were presented two candidates and made a first
selection.
Afterwards, Answers of confederates are displayed, subjects can choose again.
In the third step, we presented two items for normative and informational conformity,
to measure the reason for answer change.
It was a within-subject design.
Subjects underwent a total of Twenty Two trials in randomized order.
There were six trials with human majority, six trials with a robot majority, six trials
with a hybrid majority and 4 distractor trials.
In the distractor trials, the majority chose the same answer as the subject.
Our study sample consisted of a hundred three participants, of which we could use ninety-two
datasets.
Subjects were on average twenty-nine years old, 64 per cent were female.
The majority of our sample attended university.
I’m going to present the central results of our study.
For a detailed presentation, please see our full paper.
We measured conformity by the number of times a subject would change their first answer.
The conformity rates were not normally distributed, so we compared the rates using a Mann-Whitney-U
Test.
Our results show that the conformity rate was lowest for the robotic majority and highest
for the hybrid majority.
Consequently, Hypotheses one and two are confirmed.
To predict answer changes over the conditions, we ran a multilevel mixed-effects logistic
regression with answer change as the dependent variable (0 = no change, 1 = change).
Normative influence is a significant predictor of conformity in the human condition, informative
conformity is not Informational conformity is a predictor of
conformity in robotic majority, Normative conformity is not
Both normative and informational conformity are significant predictors in the hybrid condition
Hypothesis 3 is confirmed.
In summary, Experimental participants show the most conformity with a hybrid majority,
followed by human majority and robotic majority.
Conformity to human majority is only influenced by normative conformity.
Conformity with robotic majority is only influenced by informational conformity.
Conformity with hybrid majority is influenced by normative and informational conformity.
Our study comes with several limitations.
First, our experiment was conducted online.
This means, there was no face-to-face interaction between humans and robots.
Nevertheless, conformity can also be found in online settings.
Furthermore, it is questionable whether we used suitable robot images.
It is possible, that they were not seen as competent enough.
We asked subjects to rate the perceived competence of the confederates.
Robots were rated as less competent that human confederates.
This difference was statistically significant.
In summary, our study provides an explanation of how conformity works with majorities of
different compositions.
Scholars should consider different types of conformity in study design
Future research should investigate, if there are other robot features which might influence
conformity.
One of our key findings is, that individuals are more likely to follow the lead of humans
and robots than humans or robots alone.
This should be considered in human-robot teaming.
Thank you very much for listening to my presentation!
If you have any question, please contact me via E-Mail