hello everyone my name is Chelsea and I
am a PhD candidate at Georgia Tech I am
delighted to present our chi-23 paper on
designing a responsible AI adaptations
of ux practice to meet responsible EI
challenges
technology companies continue to
investing efforts to incorporate
responsibility in their AI Investments
to account for and mitigate the social
risks of AI technology
REI has become established as
organizational practices
much existing research on organizational
REI efforts and practices has largely
focused on ML model development process
for example some research has looked at
REI efforts devoted to understanding and
improving ml models including data
annotation practices model evaluations
data and model documentations as well as
developer facing tools to examine the
model's post-hoc
other research has highlighted
practitioners who are undertaking Aria
at work focusing mostly on the work
practices of data scientists and ml
engineers in a model development process
however Aria efforts and Industry
continue after models leave the research
lab and just as there were many
practitioners involved in our efforts
during ml model development many
practitioners are also involved in
applying ml models to AI driven products
during early stage design and
development process
among the set of practitioners apply ml
models many focus on user-facing and
user-centered aspects of Technology yet
the specific practices of such
user-centered practitioners involving
Rai have yet to be identified or
formalized and distorting them offers a
critical view into how they're adapting
to meet president challenges of Rai
to understand how ux practitioners are
adapting to meet president challenges in
REI weeks for an emerging set of REI
practices carried out by ux
practitioners when applying large models
during early stages and refinement of EI
application design
we sought to answer two research
questions first how do ux practitioners
currently incorporate and examine REI
considerations during early stages and
refinement of AI application design
given their organizational context and
second what challenges do ux
practitioners encounter in their current
REI practices during early stages and
refinement of AI application design
given their organizational context
Jonathan practices at an intersection of
user experience and REI we interviewed
15 ux practitioners and eight REI domain
experts our study took place at a U.S
side of a large technology company that
has a large AI function these two sets
of practitioners were actively involved
in addressing REI concerns both early in
design and refinement of new ai-based
prototypes demos and product features we
aim to identify and understand the
emerging area work carried out by ux
practitioners in your design processes
by investigating these practices in
light of the REI were conducted by our
experts in their formal job capacity
in our interviews we use promptmaker a
large language Model A prototyping tool
as a probe to understand how emerging AI
design and prototyping tools were
shaping UI practitioners REI practices
prop maker provides a web-based
interface to our amps enabling users to
interactively write and test lrm
problems
this is an example of using few sharp
prompts to translate English into French
ux practitioners can then embed this
within a prototype to test out a
translation feature
during the AI design and prototyping
process prop maker enables practitioners
to rapidly prototype and Test new AI
features in hours or days without
requiring significant machine learning
experience
through reflexive thematic analysis on
our interview data we identify three key
emerging practices ux practitioners
carried out to me evolving REI needs
building and reinforcing an Rai lens
responsible prototyping and responsible
evaluation of AEI applications
we want to First highlight an
overarching practice that influence our
subsequent findings building and
reinforcing and RN events and Aria
events positions REI as a reflexive
ongoing and holistic perspective that
influences practices and decisions
throughout design and development of the
EI applications
in our study we found both ux
practitioners and REI experts carried
out similar practices to build and
reinforce this Rea Islands
one practice they both carried out with
self-education and subsidization to REI
issues for Army experts a large part of
their job included sensitizing teams
across across the company through our
concerns by offering RI resources and
support such as RAF Frameworks and
guidelines REI reviews office hours and
consultations
for ux practitioners even though they
didn't have focused REI knowledge they
consider Riis as an emerging yet crucial
piece of the U.S of AI projects and
products since they explicitly and
actively sort out our resources to
better understand the issues and
integrate them into their ux practices
what specific practice that ux
practitioners carried out to further
reinforce their Aria perspective during
the design process was by implementing
responsibility lifts responsibility
lifts is a series of activities at the
beginning of design process to reinforce
REI as a lens to inspect and filter
design ideas
some examples of responsibility lists
include calling out REI issues in a
design brief or invite speakers to talk
about Rai at the beginning of a design
Sprint
for example u4 said about a design
Sprint that he participated in
REI was called out in a brief of the
Sprint as a whole to think about
responsibility for any ideas that we
come up with and what are the
responsibility implications of those
the second REI practice we identify was
responsible prototyping
in this practice REI experts and ux
practitioners lovers their different
domain knowledge expertise and skills to
surface and anticipate harms when
working with machine learning models
during the interviews Aria experts said
they were often invited by teams who
explicitly and intentionally look for
REI issues through question asking and
adversarial testing
in contrast while ux practitioners were
not trained or required to serve as an
anticipate potential AI Harms in their
day-to-day work we found that ux
practitioners were committed to surface
and anticipate harms from their unique
human focused perspective skills methods
and tools in early stage AI design
for example ux practitioners talked
about their concern of how users would
perceive a applications driven by ml
models that were often unpredictable
to mitigate the consequences of usage
mental models of AI ux practitioners
leverage different design strategies and
techniques in their design process
for example U7 told us in her interview
that there was discovered the user's
expectations of an element-based Shabbat
didn't match the actual language in
action and they decided to change the
appearance in the UI to accurately
communicate what this application is
use haven't said so we had to
reconfigure how we show these prototypes
visually and also how we communicate
about what these prototypes are and that
High Fidelity UI was vital to change up
so the team quickly responded and
changed the way that I looked and they
changed it from looking like a product
to looking more like terminal like a
very simple old school low-fi terminal
chatbot
in our study both Aria experts and ux
practitioners aspire to involve users
and other stakeholders in a process of
REI evaluation
while Aria experts largely described
their aspirations of long-term
engagement with users throughout the AI
product lifecycle ux practitioners are
carrying out this aspiration through
user testing and evaluations
your expectation is actively involve
prospective users of future AI
applications in the design process to
surface and assess REI issues
to address the uncertainty of potential
harmful model outputs project teams
would generate a wide range of
constraint-based input and output
suppression techniques that often rely
on classifiers and filters
for example you once said before the
conduct user evaluation testings they
will put in place a bunch of filters to
make sure that it's on topic but it's
not offensive that it doesn't use a set
of words or phrases that they've
specifically been
however as practitioners pointed out in
our interviews it was not always
possible to prevent harmful model
outputs completely and hence
practitioners pointed out the importance
of exploring other approaches
U14 talked about their experience of
doing user testing of an arm-based
applications at the end of a short
design Sprint
I think we had two and a half days until
we were actually testing in front of
external prospective users so it was so
fast and Rapid which is one of the pros
about it but if something did go wrong
and if the model did come up with best
suggestions or if we did have an answer
that went off the rails we could we
couldn't just shut it down all we could
do was just make the screen go blank and
hopefully they didn't see it in that
segment
based on these three REI practices we
identified in ux practitioners work we
highlight the hidden work area
practitioners are conducting to meet REI
challenges we then discuss challenges
and opportunities in designing tools and
methods to support responsible design
with large language models and finally
we talk about the challenges in future
research questions for the kai Community
to explore in terms of reconfiguring
users role in REI design and evaluation
please check out our paper for more
details
thank you so much for listening we're
happy to discuss and answer any
questions that you may have