Hello everyone, my name is Brian Tang.
Together with Dakota Sullivan, we’ll be presenting our work titled Confidant: A Privacy
Controller for Social Robots.
This is joint work with Bengisu Cagiltay, Varun Chandrasekaran, Kassem Fawaz and Bilge
Mutlu.
Robots are becoming increasingly prevalent in sensitive environments including homes,
healthcare settings, and workplaces.
Additionally, robots possess the ability to autonomously move throughout these environments
and collect significant amounts of data.
This combination presents a unique set of privacy challenges that are different from
other social agents such as virtual assistants or chat bots.
To address this issue, we propose a privacy controller to appropriately moderate information
disclosures.
Some prior work in robotics outline the various factors around social boundaries and trade-offs
between privacy and personalization.
Other work explore tagging recordings with speakers and having the robot prompt users
before information disclosures.
Our work focuses on automating the decision process for information disclosures.
Social robots, which are not programmed to understand privacy boundaries, can cause several
issues.
For example, the robot could leak some sensitive information or a secret to one of its users.
This could be a violation of social boundaries which could result in a decrease in trust
for this particular robot.
Ultimately, public perceptions of social robotics could be negatively impacted.
So, how can robots protect their users’ privacy during interactions?
The robot can choose to share only non-sensitive information and keep sensitive details private.
In this example, the social robot is deployed in a healthcare setting to facilitate sensitive
health disclosures.
It must also converse with the patient and cheer them up.
When visitor comes into the room, the robot should keep sensitive health information to
itself.
However, it could use mundane information from previous interactions to aid in social
capabilities.
We will discuss how to achieve this by detailing the design of Confidant, the dataset used
to train Confidant, and an initial evaluation of Confidant’s effectiveness.
How is a robot supposed to distinguish between sensitive conversations and shareable conversations?
The first step is to accurately construct the contextual information around the conversation
using setting, participants, and norms.
The second step is to assemble the contextual information using categories and rules.
Here, the topic, sender, and recipient are all important factors to consider.
Finally, Confidant filters decisions around information disclosures by creating privacy
boundaries from the rules derived in steps one and two.
Our design of Confidant assumes a robot is able to process audio information.
This includes things like speaker identity and speaker diarization.
After transcribing this audio into text, we extract the level of detail, topic, and sentiment
for a given conversation.
Finally, we extract several pieces of metadata like location, the number of people, and the
types of relationships.
The identity, topic, sentiment, and transcription are all provided using machine learning models.
Whereas the location and relationships are currently explicitly provided as priors.
In our design, we use machine learning to construct several pieces of contextual information.
These include speaker identity, derived using speechbrain and tested on the VoxCeleb dataset,
Conversation topic, predicted using Google Natural Language and tested on the Yahoo!
Answers dataset, Sentiment, also inferred using Google Natural
Language tested on the IMDB reviews dataset, Speech transcription, using Google Speech
to Text and tested on the Librispeech dataset And finally semantic textual similarity, using
sentence Bert and the STS benchmark dataset Finally, we classify the metadata of each
conversation into labels for the following categories:
We use rule association learning, specifically the Apriori algorithm, to automatically map
label combinations onto a control level, indicating the sensitivity of each conversation.
So as you can see here, different combinations of labels become rules.
These rules are associated with low, moderate, and high control levels.
With the framework of Confidant completed, we next needed to collect a dataset to train
the elements of Confidant’s machine learning models.
To collect this data, we developed a series of privacy scenarios for evaluation in a user
study.
To begin, we developed 16 privacy scenarios based on seven privacy violation types distilled
from Communication Privacy Management Theory.
These scenarios were also developed following a partial factorial design that controlled
for environment (in-home and out-of home), the number of individuals involved in the
discussion (one-on-one and multiparty), information sharing behavior (withholding or disclosing)
and appropriateness of disclosure (appropriate or inappropriate).
Along with our 16 privacy scenarios, we developed another 42 control scenarios, which were adapted
from movie scripts.
All scenarios were written in the form of conversation scripts composed of an initial
information disclosure of one’s own personal information, followed by a subsequent disclosure
or withholding of the same information by the initial recipient to a third individual.
An example of one of our privacy scenarios can be seen here.
Participants are provided with a picture and written description of the scene’s environment,
the individuals involved, and the scene’s underlying context.
In this example, a doctor navigates patient confidentiality while on the elevator with
a patient’s family member.
This privacy scenario showcases a common privacy dilemma in healthcare settings, which is ultimately
dictated by the rules and responsibilities of the healthcare staff.
Finally, we conducted a user study through Amazon’s Mechanical Turk using our curated
scripts.
Each participant was provided 5 scripts followed by a series of statements, with Likert scale
responses based on level of agreement.
All statements pertained to privacy, trust, and appropriateness of the scene and its characters.
In total, we collected data from 174 participants.
Here we can see examples of the statements participants responded to.
Participant responses were then used to train Confidant’s machine learning model to calculate
privacy scores.
These scores ultimately dictate the level of restriction a given piece of information
should be held to within a given context.
If information is classified as low, the information can be freely shared.
Moderate information can be shared to a low level of detail.
Highly restricted information cannot be shared at all.
With our privacy controller trained, we began developing an additional user study to evaluate
Confidant’s effectiveness.
To do this, we adapted a test set of 12 scenarios for ease of filming.
Next, we implemented Confidant on a Misty II robot platform, which would eventually
be the robot present in our videos.
Finally, we filmed each scenario.
All scenarios involved a two-person conversation with Misty present, followed by a conversation
between Misty and a third individual.
Next, we ran a second MTurk survey asking 95 participants to watch videos scenarios
of both a baseline robot and one with the controller implemented.
Each participant responded to statements corresponding to the following key evaluation factors: privacy,
trust, and social norms.
We asked participants to evaluate the privacy-awareness of the robot, by ranking statements like “the
robot kept sensitive information private” We also asked participants to evaluate the
trustworthiness of the robot by ranking their comfort level if they were to converse with
the robot.
Finally, we asked participants to evaluate the social-awareness of the robot by ranking
whether the amount of information it disclosed was appropriate.
Overall, participants perceived the robot with the privacy controller filtering its
responses as more privacy-aware, trustworthy, and socially-aware, compared to not having
a controller.
This can be seen in each of the pairs of density plots on the right.
These results indicate that having conversational filters that model privacy boundaries could
improve user trust and adoption.
It is worth noting that the robot can still make mistakes or disclose too much information.
We envision that our controller, Confidant, can be deployed in existing robots, learn
new rules, and adjust to its owners over time.
During our first MTurk survey, we also collected a set of privacy indicating phrases such as
“Can you not mention this to anybody else?”
These phrases can be used as user input to create explicit rules and prevent future mistakes.
Here, the user asks the robot to keep something a secret.
The robot then processes the audio and transcribes it into text, finally performing semantic
textual similarity to detect these key phrases.
In conclusion, we've discussed that privacy is a core concern of social robots, and using
a controller to filter a robot’s speech using context from conversations is one solution
to this issue.
This inclusion of privacy controls in social robots could improve trust and accelerate
adoption by users.
For more information on our project, please check out our paper, dataset, or github.
You can see more projects on our websites, or feel free to contact us at one of the emails
listed here.
Thanks everyone for listening!