Hello, my name is Albrecht Kurze.
I present our paper “Guess the Data” on “Data Work To Understand How People Make
Sense Of And Use Simple Sensor Data From Homes”.
We see more and more connected smart Internet of Things devices appear in the home - such
as fridges or heating systems.
These devices are equipped with simple sensors for collecting data, e.g. for temperature
or light – as shown in this example.
Smaller and smaller connected sensors make the vision of “smart dust” slowly become
reality - suitable to be integrated into nearly every mundane object or to sit somewhere pre-installed
or even hidden in your flat.
But - what do these simple sensors and their data mean to the users?
What do these simple sensors and their data reveal about the users and their domestic
activities?
While it is possible - up to a certain degree - to use automated approaches of making sense
of such sensor data we focused on human sensemaking.
It is an ethnomethodological concept of interpretation of data using the everyday knowledge of the
users and the social order on site.
Prior work has shown that human sensemaking in relation to simple sensor data can shed
light on domestic activities in detail using situated knowledge that includes the role
of context for sensemaking.
Yet, these approaches focus on domain experts and predefined goals or are limited to individual
sensemaking and households.
Little is known especially about collective human sensemaking, e.g. when looking on data
that is originating from other family members, other households, neighbors or even strangers.
In addition, little is known about the dynamics of making use of this data as often access
and sensemaking are only done subsequently.
This is where 'Guess the Data' comes in.
Our participatory research method for individual and collective data work fills these gaps.
And as we gave participants live access to their data it also allowed them for making
use of their data for own purposes.
Our paper explains how 'Guess the Data' works and we present findings of a first study using
our method that show…
How people individually make sense and make use of live access to simple sensor data from
their homes.
How people collectively contextualize simple sensor data from the home.
And, how people can be empowered to understand implications of such sensors.
To research in 'Guess the Data' we created our Sensing Home Kit.
It includes everything that is needed for data collection, visualization of the data
and to guide and document the reflection on this data.
We found the Texas Instruments SensorTags nearly optimal for what we wanted.
They are easily available, affordable, very small and lightweight.
They come with a number of simple sensors onboard, e.g. for light, temperature, humidity,
pressure etc. – and they offer wireless Bluetooth low energy connectivity and battery
based operation.
We used the openness in hardware and software to make the SensorTags suitable for our research
needs.
We modified the firmware and software libraries for longer battery runtime and reliability.
We also redesigned the housings and 3d printed them in different colors to make them easy
distinguishable for participants.
We planned for a first try-out a field study with different study phases that I will explain
followed on.
We sampled for our initial study participants of 9 households in three groups.
The groups show differences in age, relation of the participants and spatial proximity
of living.
In the beginning of a ten to fourteen days data collection we installed the sensors in
three predefined sensor positions in participants‘ homes that promised in pre-studies to show
a lot of ongoing domestic activities.
We instructed the participants how to access their sensor data on the pre-configured tablet
and gave them full access to their data.
We also wanted the participants to reflect on their own data collection.
Therefore, we asked them for documentation and annotation of own data, activities and
events as well as what they find to be interesting observations.
We used a diary like documentation approach.
We encouraged the participants to make screenshots and to annotate them – on paper as well
as digitally.
In the week after the data collection we as researchers did an own kind of data work.
We browsed through the collected sensor data and annotated material reconstructing the
situated knowledge for interpretation.
We looked for interesting data sections that we wanted to use for a group discussion as
stimuli for collective data work.
We selected for each group around a dozen relevant data sections, from a few hours duration
up to several days and included data of all participants of a group in the selection.
We anonymized the data and prepared large format prints of data graphs in the same visualizations
as the participants has used before in their individual data work.
One week after a data collection in the households ended, we invited our participants in their
sampling groups for group discussions of approximately 90 minutes duration.
We used the printouts as shared work sheets for collective data work.
We asked participants to 'guess the data': what they see in the data, where the data
comes from, and which everyday experiences they could identify.
We integrate immanent questions in the evolving discussion between the participants when necessary.
When the participants came to a common sense, we moved on to the next print.
This brings us to the findings.
First part of our findings confirms the inner workings of individual data work, identified
by previous work.
Therefore, I will be very brief on this.
In addition to existing findings our findings show that the concept of data work is also
applicable for lay users.
We found that in conjunction with situated knowledge and timely access to the data, it
is often more an assigning of cause than a speculation when reading the data and annotating
it.
The collective data work in the group discussions work worked extremely well as it offered us
a good understanding how the collective sensemaking works.
The printouts of data sections worked as expected as catalyst of plausible data episodes – about
the "what and who".
The participants mostly assigned the anonymized data correctly to the originating home and
they used their situated knowledge as expected.
Often the daily routine was used as backdrop to interpret the data but also deviations
of this routine were used.
Sometimes this lead to misconceptions and false interpretations of the data.
By collectively interpreting data with other participants, reasoning became an intersubjective
task.
The unfolded discussions can be understood as negotiations towards collective sensemaking
patterns.
We often observed that even if a single participant might fail to interpret data, the participants
might collectively come to an accountable interpretation of sensor data with intersubjective
plausibility.
Participants became aware in the discussions that sensor data might even reveal intimate
details about their personal life.
Participants noted that their presence or absence in apartments and rooms could easily
be determined through light and motion sensors.
Moreover, the participants realized that they could not only make sense of own data but
also of other participants data.
Often the collective data work in the group discussions initiated tellings about data
and data uses not even shown the data examples.
This bought up episodes of obvious uses, such as damp prevention but also about more critical
uses or even misuses.
Multiple times the participants used the sensor data as evidence and proof, e.g. to problematize
behavior in their household or aimed to use the sensor data to enforce a change in habits,
e.g. to reduce energy waste.
A striking finding is that we also found examples of use with surveillance as purpose.
The sensor data was used at least twice with full intention to surveil family members.
This was nothing that we anticipated.
The participants used the sensor data intentionally asymmetrically.
This way simple sensor data became a power differential within families.
This vignette illustrates this misuse of the sensor data as means of surveillance.
The participant used the sensor data to surveil her spouse and to convict him of lying.
This example shows: The less abstract and more concrete threat for an individual's privacy
might not come from an anonymous third party Big Brother but from an actual kindred mother
in the family.
This brings us to the discussion.
Based on our findings we discuss how data-driven guesswork easily persuades people to speculate
with data and about data.
A certain activity may not be read from data but read into the data.
We also discuss implications for intersubjective sensemaking.
Even a little bit of situated knowledge can turn a guess into an educated guess.
This allows plausible intersubjective sensemaking and interpretation of data from other households.
And this intersubjective interpretation on the data is then often easily taken as objective
truth.
We especially discuss implications for privacy.
Our findings regarding simple sensor data in the home show that even if the sensors
are just simple environment sensors, their data can reveal a lot of sensitive and personal
information about the residents.
Combined with situated knowledge about the housing situation this data can be used to
identify a certain person and recognize their activities within the household.
The perception of the privacy aspect of the sensor data is ambivalent.
The participants understood that surveillance becomes possible and wanted to be able to
control who gets access to the data.
At the same time, they did not felt surveilled during the deployment and could imagined using
it to surveil others.
This brings us to the conclusion.
We introduced 'Guess the Data' as an innovative participatory research method for individual
and collective data work.
'Guess the Data' enables insights how people make sense of and use of simple sensor data
from homes.
Suitable methods such as 'Guess the Data' can help HCI to better identify, understand and
address the emerging implications of simple sensor data in the home.
Findings of our initial study using 'Guess the Data' clearly show, that simple sensors,
their data and human sensemaking are not harmless by default: interpretation as well as misinterpretation
and use as well as misuse within situated knowledge are to be expected.
Implications of simple sensor data in the home do not only exist in theory, they occur
after only a few days of application.
Thank you for listening.