- Hi everyone,
Thank you for attending my talk.
My name is Ashley Boone.
I'm a PhD student at Georgia Tech,
advised by Dr. Christopher Le Dantec.
Today, I'm going to be sharing with you the highlights
from my work with the partner organization
in the southeastern US
and describe how their data practices embody
a politics of care.
In this presentation, I'm going to share
with you key takeaways from our partnership
with an organization called Immigrant Affairs.
Immigrant Affairs serves immigrants
in a southeastern US city.
They operate out of the mayor's office,
which means that they're part of the municipal government,
but their funding model actually more closely follows
that of a nonprofit
and they participate in continuous cycles
of grant applications to ensure funding
The organization employs five full-time staff
and nine community navigators.
The community navigators are hired as residents
of the communities that are served by Immigrant Affairs
and their role is mainly to interface
with residents from in these neighborhoods.
One of the central services provided
by Immigrant Affairs is food assistance.
This program was developed
in response to COVID-19, which exacerbated food insecurity.
The food distribution model is designed to directly
bring food into neighborhoods experiencing food insecurity.
The organization viewed this as
both an opportunity to fill an urgent need
and an opportunity to build relationships
with community members.
We chose food assistance as the center point
for our study because the data system was developed
in parallel with the food assistance program
and because this was a place
where most residents encountered the data system,
creating visible encounters.
We used ethnographic methods
to understand data practices at Immigrant Affairs.
I conducted 28 hours of field work at food distributions
and we also conducted interviews and focus group
with staff members
to understand the behind the scenes data work
and to get a perspective
on how the data system had changed over time.
We use this data
to conduct an inductive thematic analysis,
pulling out key themes from our observations
and our interviews with staff.
At this point in the project,
a few key themes started to really stand out
in understanding data work at Immigrant Affairs.
First, we saw this really intentional decision-making
around the data practices in the organization.
We also observed a deep commitment
to providing material relief
and protecting vulnerable populations.
Lastly, we saw the high value that the organization placed
on the relationships with residents and community members.
We interpreted these characteristics as a practice of care.
In order to understand data work in the organization,
we turn to literature on the ethics of care.
Ethics of care originated from feminist psychologist
and ethicist, Carol Gilligan in the 1980s.
Ethics of care prioritizes relationality, reciprocity
responsiveness, and plurality.
Care has been used in STS to highlight matters of care
and in describing carrying processes like tinkering.
Care also comes up in HCI,
in discussions of healthcare, making, and IoT.
Additionally, care has emerged as a political theory.
In the book "Caring Democracy," Joan Toronto argues
that care should be central to a democratic society.
She describes five ethical qualities
that characterize a caring democracy,
which include responsibility, competence,
attentiveness, responsiveness, and solidarity.
Taking a political approach to care,
using these qualities presents care
as an alternative organizing principle
to neoliberal capitalism.
Returning to our partner organization,
using "Democratic Caring" as a lens
highlights several key social and technological features
of data work at Immigrant Affairs.
I'm going to highlight a few of those features here today.
First, Immigrant Affairs values
and prioritizes relationships with community members.
This is operationalized in their data practice
by both social and technological features.
First, the organization chooses
to track success metrics
that reveal community relationships.
The main purpose of data at Immigrant Affairs
is to record the number of people served
by the organization.
Instead of measuring the weight of food distributed
or the number of meals served,
successes defined by the number of people
that the organization reaches.
In order to keep track of the number of people served,
Immigrant Affairs requires that all attendees check in
with the community navigator before receiving food.
The organization recognizes
that this is a practical necessity
that allows them to keep track
of the number of people served,
but they also know
that it creates moments
of one-on-one connection between peers
and they use that to build trust
and check in with members of the community.
Therefore, attentiveness becomes part
of the standard procedure at these events.
Secondly, we see a commitment
to protecting vulnerable populations that is embodied
in the data practice.
Immigrant Affairs intentionally does not ask for data such
as citizenship status,
which could potentially cause harm to residents
or could deter them from interacting
with Immigrant Affairs in the first place.
If people show any discomfort
around answering questions that are part of the intake form,
the staff are trained to skip those questions.
Staff at the organization explained to me
that it was more important to them
that residents were able to access services
and that the organization was able
to maintain their relationship with community members.
These things were considered more important
than collecting reliable, consistent data
about every person.
In both these cases, non-collection is part of the work
of providing competent care
for immigrant communities in the southeast.
Both of these cases show how non-collection is part
of the way
that Immigrant Affairs competently serves immigrants
in the Southeastern US.
Lastly, through ongoing tinkering
and changing forms and changing data practices,
we see a responsiveness to community needs.
For example, the organization respond
to confusion about Likert scale questions
by replacing Likert options with yes, no, and maybe.
This response required a close sociocultural attention
and a willingness to respond to the needs
of the community they worked with.
Using care as a lens
to understand data practices
that Immigrant Affairs reveals nuance, flexibility
and sociocultural awareness in our partner's data work.
We do not argue
that the organization has overcome the inherent biases
and value judgments that are produced by measurement,
but we do think
that the organization's data practice has been formed
through a careful attention to the politics of measurement.
In both the design of the technical tools
and the social practices that facilitate data collection,
we see that Immigrant Affairs
has carefully designed a system
that enacts a politics of care.
If we believe that all data systems embody political values,
we argue that the political stance embodied
by data work at Immigrant Affairs is one of care.
So how might we design to support caring data systems?
Based on our work with our partners
we highlight three suggestions
for building data systems for care.
First, we believe that caring data systems
must prioritize inclusivity,
even when it comes in conflict with other usability metrics
like efficiency or even legibility.
In order to provide data collection in the field,
Immigrant Affairs uses iPads and smartphones
to enter data on a platform that is designed for desktop.
Additionally,
they have a workaround
that allows them to offer the forms
in both Spanish and English
by writing the questions first in English
and then in Spanish.
These workarounds make the forms difficult to read,
but the system allows the organization
to meet residents in the field
and to hire non-English speaking community members
as community navigators.
Second, we believe that data systems for care
must work to minimize resource drain.
The primary factor in deciding to use Charity Tracker
which is the platform that Immigrant Affairs uses
to collect data, was the price and the scale
of the platform.
While many data management systems are built
for large companies with teams that manage
and work around the data,
these models restrict what kind of organizations
can participate in data work.
We think that data systems for care need to be able
to minimize resource drains so that they can be used
by a large variety of organizations.
Lastly, we think caring data systems resist
quantification wherever possible.
At Immigrant Affairs, we see this resistance
in flexibility and data collection.
The refusal to collect or even request sensitive information
and storytelling around data in grant applications
Non-collection flexibility and storytelling are all methods
for resisting the reduction
in universalization inherent in quantification.
We hope that these suggestions provide a starting place
for rethinking data systems that support data practices
with an intentional political stance towards care.
I'm going to end the talk here for today.
Please feel free to reach out to me
with any questions or comments, and thank you for listening.