Welcome and thanks for taking the time to view our presentation.
This paper is about artificial intelligence, legibility,
design, and research. AI is a metaphor. It's a metaphor for humanity's ancient
desire to imitate and create life. While our attempts to imitate the wonder of
intelligence have failed, through those failures AI's metaphor has grown to
represent many more ideas including robotics, expert systems, talking donkeys,
neural networks, predictions, modeling, data science, machine learning, deep learning.
But we live among the two sides of AI's definitional dualism. By this
we mean that AI is science fiction, AI is chappie, Skynet, AI is A-I-Robot, AI is
the singularity, super intelligence, AI is forever a possibility but it's never a
reality. But on the other side AI is a reality, AI is mundane, AI is
everywhere and it's really very real: it's Facebook, it's Google, it's your
phone, it's in your house, AI is unavoidable, AI is everyday,
AI feeds off data.. data which we leave around us. Electronic traces of our lives
and our interactions. The first step in making AI legible is to understand this
dualism: AI is a grand dream and ubiquitous reality... all at the same time.
Accepting and apprehending these simultaneous truths is our foundation.
But what else is there to say about AI and legibility? What would it mean for
the AI in my camera, my speaker, on my car to be legible, and, do we even care?
Well we do care. Almost daily there is a new ethical, responsible, just, accountable
trustworthy, private, reliable, framework, or guideline, rulebook, or checklist,
system, configuration, architecture or policy or standard for AI. And each one
is a reflection of the fact that we care. but for these to work, legibility is key.
To see what is right first we must see. And, we see signs and symbols everywhere.
They're a staple of our world. Stop, go don't, do, love, hate. Each one communicates
to us. It makes things that might not, but need to be, legible. In computing
these icons are commonplace forms, they quickly tell us what we need to know. So,
what would, could, or should icons for AI look like? What might we
learn by trying to create them? And what are the right questions to ask
explore, reframe, and research? The crux of research through design is to create
something, and through the process to learn. In the case of the socio technical
challenges around AI, research through design is a particularly useful approach
because it allows us to reflect the aforementioned proliferation of guidance
and frameworks for responsible understandable or ethical AI. It allows
us to incorporate ideological and technical elements alike and allows us
to do this while tangibly moving the conversation forward through a material
engagement with the issues. In the sea of complexity arising from
the relationship between AI, data, and people, design research has the unique
ability to materialize new meaning and ways of looking. As we began the design
process it became evident that AI's dualism is reflected in the prevailing
imagery; brains, neurons, and visions of inanimate machines combined with the
fleshiness of humans, dominate the space. So we thought about this. Elsewhere, we
considered how famous marks convey meaning symbolically; safety, security,
quality, good value, or with good values. So, our designs began to take shape in
three different ways. Pictorials, brands, and abstract symbols.
To these styles we explored how six concepts, each one a factor of an AI
systems legibility, may be communicated. Processing location; scope of learning;
training data provenance; training data type; and how much work am i contributing
to the system simply by using it. An idea we 'termed intrinsic labor'. AI processing
takes place on board. AI processing takes place in the cloud.
The AI is static; no learning through use. The AI learns continually through use.
Proprietary training data. Training data unknown. Open training data. Photographic,
positioning, audio, and biometric training. Our explorations led us to test the
prototypes in combination and across different styles. These designs
are not ends but they're means. They're means to find, ask, and illuminate the relevant
questions. Subsequently we expanded on the prototypes through evaluative workshops,
deeper design fictions, and refining which AI attributes are relevant in
different contexts. The next trick is to extend the border of our magic circle.
(the realm of the design, fiction, and the research) and to encompass real
contexts, people, data, and consequences. The
work continues. AI has become part of the fabric of our world.
It's canny and uncanny all at the same time. The race to discover, disseminate
and dissect, what is acceptable AI is, is on.
This work is a manifesto for design research's role in that race. And with
that said, if we look beyond the facade of AI's definitional dualism and the
buzz of AI's own hype, this work is really about how design can help mediate
between technology, and society. Thank you very much for listening. We invite
comment or critique. And, hope you enjoy the paper.