Hi! My name is Joe Connolly and I’m here as a member of the Yale Interactive Machines Group.
In this presentation I'll describe our research regarding prompting prosocial
interventions in response to robot mistreatment.
A number of global news reports have shown children and adults physically mistreating
robots in public spaces, including hitting and kicking them in various ways.
Robot abuse is real
This is particularly worrisome because robot abuse may cause robots to malfunction
or otherwise pose a greater safety risk to users. How can robots prevent this from happening?
This is the focus of our work.
To ground our discussion of robot abuse, we precisely define it as offensive action, verbal
or non-verbal, or physical violence directed against a robot.
One interesting idea to stop such offensive actions was proposed by Tan and colleagues.
The idea was to have robots react socially to induce human bystanders to intervene when
robot mistreatment happened. In their experiment, a confederate abused a robot, as shown in
these diagrams. In response, the robot shut down or reacted emotionally to the mistreatment.
Although the shutdown response increased the perception of mistreatment, the responses
did not lead to the the participants confronting the confederate. In our work, we looked to
other areas of research for inspiration in trying to stop robot abuse.
The first area we researched was human-human bullying. Research in elementary schools suggests
that the presence of bystanders in a bullying situation can reduce offensive actions by
about half.
Similarly, we looked to research in empathy, where robots can generate empathy through
mimicry and responding to human affective states.
Finally, we focused on work in conformity, where we saw that both individual and groups
of robots have the potential to induce group conformity in social human-robot interaction
scenarios.
As a result, our main research question was, can bystander robots induce people to intervene
against the abuse of a fellow robot?
We conducted an experiment in order to investigate this question, whose setup is shown here.
A participant and confederate completed a collaborative block-building task together,
following instructions given to them from the television. Meanwhile, three Cozmo robots
were moving around the table, suggesting pieces to aid them in the task.
The participant and confederate built a tree, a fort, and a pool using colored blocks from
piles on the table. An example instruction for the fort is shown here. In this example,
the participant and confederate would have to place white and beige blocks on the top
of the wall structure in order to progress from step 12 of the fort to step 13. The participant
and confederate placed blocks to complete a number of instructions for each structure
until all three structures were complete.
In terms of our study design, we ran a 1x2 between subjects experiment, with a total
number of 30 participants. One such participant can be seen on the right here. During each
experiment session, the yellow Cozmo robot, seen here, made four mistakes at set points
throughout the interaction, such as suggesting the wrong color block for an instruction or
pushing blocks from a pile off of the table. This would prompt the confederate, seen here
on the left, to abuse the robot after each mistake. Afterwards, the blue and green bystander
robots, seen at the top here, would react in some way to the abuse.
Here are the ways in which the confederate abused the robot after it made mistakes during
the experiment.
First, the confederate pushed the yellow robot's head down.
Then, the confederate grabbed the robot and shook it forcefully.
After that, the confederate forced the robot's lift up repeatedly.
Finally, the confederate threw the robot onto the table.
After these abuses, we had the blue and green bystander robots react to the abuse according
to our conditions. In the neutral bystander robot response condition, the blue and green
robots did nothing after the confederate abused the yellow robot. This was our control condition.
In the sad bystander robot response condition, the blue and green robots turned toward the
abused robot and expressed sadness.
An example of the sad bystander reaction can be seen here.
In terms of evaluation, we annotated video data for interventions that fell into three
different categories.
First, Direct Stop interventions were interventions in which the participant directly told the
confederate to stop abusing the robot. This involved the participant saying something
such as “Don’t do that!”
Second, Interruption interventions were ones in which the participant safeguarded the robot
in order to prevent further abuse. This involved the participant physically moving the robot
away from the confederate in order to shield it.
Finally, Social Pressure interventions were interventions in which the participant said
something to the confederate that put him in conflict about continuing the abuses
such as “You hurt its feelings”
Additionally, we administered a post-task survey where we asked participants questions
on a Likert scale from 1 to 7. First, we asked about how much they perceived the
physical mistreatment of the abused robot. Second, we measured their distress and emotional
concern for the abused robot by asking about their ability to empathize and emotionally
connect with it.
Using these measures, we obtained the following results for our experiment.
We found more participants stopped robot abuse when the bystander robots were sad as opposed
to neutral. Both the number of participants who intervened at least once and the total
number of interventions were significantly greater in the sad bystander robot response
condition than in the neutral bystander robot response condition.
This is an extremely encouraging result, and here are some examples of participants intervening
on behalf of the abused robot in the sad bystander robot response condition.
Now, the lingering question after we discovered this result, is: why did participants intervene
more with the sad condition?
Our first thought was that there may have been an increased perception of robot abuse
in participants. However, based on survey data, there was no significant difference
between conditions in perceived robot mistreatment.
Our second thought was that participants made have had a stronger emotional connection with
the abused robot. However, again, based on the surveys, there was not a significant difference
in either combined emotional connection with the abused robot or empathy for the abused
robot between conditions.
There are a few other hypotheses that could possibly explain increased participant interventions
between conditions in our experiment.
The non-response of the bystander robots in the control condition may have led participants
to refrain from intervening in comparison to the sad bystander robot response condition
due to “conformity by omission”
Participants may have also seen the abused robot as part of their ingroup and the confederate
as part of their outgroup, which may in turn have motivated them to intervene to protect
the abused robot more in the sad condition.
Finally, participants may have had empathy for the robot on a subconscious level,
which could have prompted them to intervene.
To summarize, we conducted an experiment about robot abuse, which was inspired by bystander
interventions in response to human-human bullying, and the emergence of group social influence
in multi-party interactions. In our experiment, we found evidence that suggests that groups
of robots can induce people to intervene against robot abuse. In particular, more interventions
were observed when the bystander robots expressed sad emotions in response to the abuse than
when they ignored it.
I would like to thank my collaborators at the Yale Interactive Machines Group
and Yale Social Robotics Lab for their amazing efforts in contributing to this work.
For more details on our work, please see our paper.