Hello.
I'm Morgan Scheuerman, and I'm going
to talk to you about the work that colleagues and I did
at University of Colorado Boulder
on how race and gender are constructed in computer vision
databases.
We all experience the visible external features
associated with race and gender when
we interact with the world.
As a result of these interactions,
we also hold our own affinities with race, gender,
and a multitude of other identities.
Race and gender have also always been incorporated
into technical systems.
In the US Census to Facebook, we see technical representations
of these identities everywhere.
They have also become increasingly
embedded into databases used to train machine
learning-based algorithms.
Perhaps the most salient example of this
is in facial analysis technology.
Within the last decade, facial analysis technology
has quickly transitioned from a theoretical research problem
to a commercial reality.
Facial analysis technology is already widely used.
For example, it is employed by law enforcement,
or its used to track consumer behavior and demographics
inside of stores.
The outputs produced by facial analysis systems
are premised on their training and evaluation data--
the images used to teach the system what
a subject looks like.
We analyzed how race and gender are represented
in image databases and how those representations are derived.
What categories are employed to represent race and gender?
What sources are being used to derive race and gender labels?
How are database authors describing the annotation
procedures for race and gender categories?
To answer these questions, we took
associative historical approach in our analysis.
Historically, formally classifying race and gender
has also resulted in harm.
For example, the differential access to health
care for marginalized genders and the targeted extermination
of racial groups.
We analyzed race and gender in training databases
from a critical discursive perspective.
Specifically, we investigated how race and gender
were codified into 92 image databases, many of which
are publicly accessible and widely used in computer vision
research.
We saw vast inconsistencies across racial categories
and methods for determining the race of a person in image data,
with some databases employing three racial categories,
like MS-CELEB-1M, and others employing seven,
like 10k US Adult Faces.
We also saw historically problematic categories
for race that would often be viewed as inappropriate.
Meanwhile, as previously documented
in our own prior work, we found consistently binary categories
for gender.
We saw different forms of this binary,
such as "M" versus "F" in the Air Database, or "Male"
versus "Attractive Woman" in Pubfig.
At the same time, we found that the majority of image databases
rarely contained any kind of underlying source or reference
for how those identities were defined.
Further, when images are annotated with race and gender
information, database authors rarely
describe the process of annotation,
such as who is doing the annotating
and what visual cues are leading them
to make specific judgments.
The lack of transparency around race and gender
made it difficult, if not impossible,
for outsiders to understand how identity-based decisions are
being made when constructing these databases.
As such constructions are important,
categories of race and gender have
been associated or historically-linked
to politics of oppression.
Race classifications in the United States
have determined every aspect of a person's life.
Gender classifications have determined
who can get access to certain spaces and health care.
Such examples of older technologies of classification
are portable to new facial analysis technologies.
We posit that the lack of similar engagement with ground
truth race and gender data undermine
the very purpose of image databases
to be usable, reliable, and accurate.
Further, it ignores the associative historical reality
of race and gender.
We thus observe race and gender categories
that are socially and historically complex,
portrayed as obvious static, and apolitical.
In our paper, we discuss opportunities
for improving the current state of image database
to construction and documentation.
For example, by actively acknowledging
the associative historical context the databases stem
from, we can attempt to avoid replicating
the harms done by historical classification systems.
By explicitly stating the limitations
of use of this data, we can help protect marginalized groups
which might otherwise be harmed by the deployment
of facial analysis technologies.
And by regularly revisiting and revising databases
and retracting those that have been deemed harmful,
we can prevent their further entrenchment
into future systems.
Thank you for listening and feel free to reach out
to me with any question.