Seeing through Things,
Exploring the design space of privacy-aware data-enabled objects.
This paper is published in ToCHI Journal in 2022.
This research is held by the collaboration between two universities.
The authors are Yu-Ting Cheng, Mathias Funk, Rung-Huei Liang, and Lin-Lin Chen.
So, the first question you may ask, what is data-enabled object?
Data-enabled object is a type of research object that has data
capturing capabilities and can be integrated into daily activities.
To capture data about people's detailed whereabouts, behaviours, and routines.
These objects can provide data
perspectives on everyday life for contextual design research.
For example, studying people's unconscious
or private behaviours, prompting people to have more grounded
responses during interviews, or engaging people to discover hidden
practice in their familiar routines through data perspectives.
However, data-enabled objects are still computational devices.
Unless explicitly programmed, they cannot adapt their data collection
mechanisms to react to different situations.
They can be insensitive and raise privacy concerns from participants.
While researchers may provide information letter and consent form for participants
to read and sign. They may oversimplify
the privacy requirement, which is not just a yes and no question,
but it is context dependent hidden in our social norm.
As a result, design researchers can face
a dilemma in employing data-enabled objects.
They may ask: Are the risk worth the benefits?
So this work aims at exploring how privacy
design can address the tension between participant privacy and ethnographic needs.
Apart from privacy guidelines
and legislation pertaining to data protection, ideas about privacy remain
highly subjective, varying from person to person.
To better understand an individual's privacy requirements,
we create in-situ experiences with data capturing devices to study real reactions
and identify potential privacy design space.
We invited 75 bachelor-level design
students, which were grouped into 18 teams
to actually design, build, and deploy data-enabled objects into the practice.
We looked into their design process,
reflections, and feedbacks from the participants
to construct design space for privacy-aware data-enabled objects.
We developed a toolkit to provide design researchers the technical support.
Furthermore, to better identify common
and different points from multiple cases, our toolkit was built on an existing
example to ask them to redesign the same types of data-enabled objects.
And this toolkit is the Connected Peekaboo Toolkit, CPT.
So we built CPT that includes first
a camera called Peekaboo Cam, which has an interactive closure,
a notification sound, and an interactive flag.
We upgraded to be embedded with additional local WiFi router and data storage memory.
Design teams
were asked to redesign this camera into a privacy-aware object,
but designing additional sensing objects to track changing situations.
To do so, design teams received two ESP boards and one processing code.
They can attach ESP boards onto the sensing objects they design.
This board transfer the sensing data to the Peekaboo.
At the same time,
they can add the processing code via the local Peekaboo hotspot
to remotely control the camera using these sensing objects.
And finally, a data-sharing interface
called Data Canvas is embedded in the Peekaboo Cam.
Design teams can connect to the interface
via WiFi hotspot enabling debriefings of all captured data
for both design researchers and participants.
And with the CPT, we successfully enabled
18 different privacy-aware system designs in 18 different home contexts such as
balcony, kitchen, door entrance, and bedroom.
And they explored how to design data-enabled objects that can engage in human
activities to capture contextual data without intervening human practices.
And how can the object provide
stakeholders with the desired level of control?
And then finally, how to make objects better enact their
autonomous capabilities as context changes.
So we conducted thematic analysis and identified five different design spaces
including Form,
Notification of data capturing,
Observational perspective,
Interactions of data capturing,
and finally, the Data processing and mapping.
And these findings can be seen in our paper.
So finally, our work identified that our
CPT created a controllable intervention which facilitated a lived,
ethnographically observable experiences in a domestic context.
The CPT made researchers and participants both take an active role in determining
what to observe and capture during the research phases.
Yet the CPT still provided openness for designers to address the tension
between ethnography and privacy needs through interaction design.
As such, designers gain insights
into privacy that can only be identified in real-life context.
And finally, we also reflect that privacy
is often seen as a constraint for designers and researchers who need data.
However, from our exploration with 18
teams, we found that privacy design should be seen as a driver rather than a blocker.
Our student case showed that participants can become collaborators,
capturing and sharing unexpected photos with data-enabled objects.
This enrichs the data collection with
additional semantics and participants become co-ethnographers.
Taken together, our contributions present
an optimistic outlook on privacy-aware practice in design ethnography.
Future work could explore more different
types of controllable intervention to encourage designers to design privacy
as a driver and address the complex interplay between data collection
interests, data ownership, and sensitive information.