Hello, I'm Gabrielle Benabdallah and I am a PhD candidate in the department of Human
Centered Design and Engineering at the University of Washington,
and today I will talk about Slanted Speculations: Material Encounters with Algorithmic Bias.
The projects presented in this pictorial stemmed out of a course that explored the topic
of bias in artificial intelligence from a material perspective. The projects and
reflections of the authors were also documented on the online platform onthebiasthinking.com which I
encourage you to visit if you're interested in speculative approaches to bias in AI.
People encounter AI bias daily, for instance through
the algorithmic curation of content on social media platforms, streaming services,
when sending out a resume to a company which relies on software to triage candidates, or in
financial scenarios like direct lending where AI determines the credit score of an applicant.
But in all of these scenarios what is meant by bias can be hard to pin down.
As designers, some of us grapple with digital applications that are biased against us;
others of us develop those biased applications;
and most of us contribute to systems that perpetuate the very bias we try to design away.
Bias is widely defined as unfair prejudice against a person or group but how that manifests
can be subtle or manifold, often systemic, and therefore difficult to grasp and use.
When considered in a material context however,
bias refers to the direction diagonal to the weave of a fabric. When we cut on the bias in textile,
we can change the way the fabric behaves, giving the material more softness and elasticity,
creating drapery and movement in the way the fabric falls. The projects presented in this
pictorial put material bias in conversation with cultural bias using textile as a metaphor.
Through these projects, we developed a slanted approach to speculative practice which we call
slanted speculation. Where speculative design tends to ask "what if" questions,
slanted speculation tends to emphasize questions of "yes, and?" The projects presented here do not
outline new machine learning systems but instead use them to create new and different orientations,
oblique perspectives on computational and biased encounters.
This research builds on the work of historian Marisa Fuentes and English scholar Linda Brodkey.
In this presentation, we focus on three pieces out of the nine featured in the pictorial.
Each of these pieces engage with a different form of how machine learning bias might manifest.
These forms are tactile, visual and textual.
The pieces grouped under tactile speculations use
physical media such as fabric, food or found objects to reflect on encounters with AI bias.
The prompt that guided these projects encouraged physical and sensory investigations of machine
learning, enabling the authors to think about data in tangible and situated ways
Such a project is author Kathryn Reyes' piece called "Touch",
a soft materialization of smartphone use data.
Through this piece, Kathryn explores the bodily origin of computational engagement: the swipe.
Kathryn used data recorded by the Screen Time feature of her iOS device and materialized the
data through cut pieces of felt. Each piece of fabric is a visualization of the small gestures
that allow us to navigate online space. "Touch" is a reminder of the ways bodies are always already
involved in the thickness of computational systems and how they possibly generate and spread bias.
Another form of AI bias is visual. The slanted speculations in this section
make use of the graphic possibilities of machine learning: GAN imagery, deep fakes,
and other algorithmically generated images. The projects presented in this section were prompted
by an invitation to engage with these visual tools to reinvent artifacts and craft new narratives.
One of the projects in this category is author Chariell Glogovac-Smith's
"HairStyleGAN". The piece is an experimental encoding of text in algorithmically generated
photographs of braided hair. The algorithm used by Chariell takes in a short phrase
as input and "translates" it into a seed. Seeds are internal coordinates within a StyleGAN2
vector that allows for the discrete location of a particular section within the model.
The resulting image encrypts a representation of a cultural artifact.
In this particular piece, Chariell hides excerpt from American writer Audre Lorde's poem "A Woman
Speaks" in views from the back of a person's head. To create the imagery, Chariell trained a GAN
model on a dataset of braided hairstyles. The visual results are arresting, pushing the viewer
to look closer rather than recoil from the erasure of blackness, as Audre Lorde's poem suggests.
The last form of bias explored in this pictorial is textual. Textual speculations engage with the
algorithmic manipulation of text. The projects in this section think of text expansively as
a technology of inscription that encodes specific worldviews and particular narratives
around gender, social hierarchies, human and non-human relations, and design paradigms.
For instance, author Lacey Jacoby's piece "(De)contextualizing Language
Through Google Translate" is a prototype system that translates English into Japanese
and then asks for highly specific, contextual data that may alter the translation. This project stems
from Lacey's realization that depending on the language such as Japanese, Korean or Indonesian,
the natural language processing model underlying Google Translate made assumptions based on the
sexuality and gender of the person spoken to. In her prototype, the system asks questions regarding
the speakers' age, job titles, relationships, context in which they're speaking, and the mood
of the conversation to make decisions about the appropriate level of politeness required.
With this system, Lacey seeks to complicate questions of language as data:
When does data work as context? And when does context work as bias?
Which voices inform translation and which voices are left out?
Finally, given the complexity of language, what constitutes "enough" data?
These works, together with the other pieces featured in the paper,
engage speculatively and materially with topics of algorithmic bias.
We reflected on these projects and identified strategies for slanted speculation, an approach
that treats AI bias as a tool for tilting conventional encounters with machine learning.
Moving away from the urge to frame interactions and systems as problems to fix,
slanted speculation retains the "problem" in bias; not resolving it but rather tilting it so that
it can cast a different light, generate different encounters. There is still much to explore in this
regard and we hope that these projects encourage designers to consider the bias of machine learning
as another possible instrument to investigate,
not only to investigate what AI systems could be but also reformulate what they already are.
Thank you for listening!