Welcome to our talk, "From Explainability to Ineffability?:
ML Tarot and the Possibility of Inspiriting Design". This is a
project developed by Caitlin Lustig in collaboration with me,
Daniela Rosner, from the Human Centered Design & Engineering
department at the University of Washington. With increasing
attention paid to the role of interpretability and
explainability in machine learning systems, the field of
design research is searching for new, different tools to design
accountability. But making an algorithm understandable does
little to account for designers' situation or status. Reflecting
on the situation of the designer, this work uses
combined design inquiry and speculative approaches to
creatively rethink the question of AI explainability. In
particular, we ask how a reflexive arts tradition can
help us embrace unexplainability in AI design. To explore this
question, we describe a process of developing and using a
machine learning, or ML, generated deck of tarot cards.
Tarot is a tool for reflection and divination often composed of
78 cards, each connected with an archetype and associated story.
Players typically pull a small number of cards at random from
the deck and use their text and illustrations to prompt
introspection. Attending to a person's past, present and
future, some people read tarot for others, while some use it as
a mode for self-reflection. In our process, we draw from an
emerging tradition of tarot-based design research and
speculative futuring to explore the use of machine-generated
cards among design researchers. Our work draws from a range of
literature on algorithmic explainability, ineffable
design, and tarot as a reflective inquiry. In
particular, we explore the important but under-examined
connections between explainability and ineffable
design. What kinds of insights can we generate when we embrace
the unexplainable and how might one--in the words of performance
studies scholar Bettina Judd--open oneself to
possibility beyond the ineffable, to place oneself
relentlessly in the curious space of spiritual inquiry? By
working in the emerging tradition of design inquiry
toward divination and the ineffable, we explored this
potential of inspiriting design in AI. To create the ML Tarot
deck, Caitie engaged in the multi-stage process. This
involved using AI to generate descriptions of the cards, and
then using another AI algorithm to generate the images on those
cards. This included the use of GPT-2 as well as the VQGAN+CLIP
algorithms. The resulting ML Tarot is composed of 78 machine
learning-generated text and image pairs. To understand a
reading with ML Tarot, consider this three card example where I
selected three cards representing past, present and
future.
For The Hierophant, I look to the past, writing, "The face
asks or maybe invites me to consider mutability--the
changing nature of memory; what seems at first so stable and
complete, but never quite so." For the Ace of Swords, I look to
the present, writing, "So much hierarchy in this image. The
wall becomes a series of steps. I'm a small in figure on the
right climbing higher, even reaching out to find
something--but unclear what I find or where I find it." For
The Tower, I look to the future, writing, "The landscape has a
misty quality. It's a blue sky but the water color character
makes it seem hazy and serene in the image, but only as an
onlooker. I want to look at the bright pink light below, but my
focus is drawn to the tower in the sky." These reflections set
up for longer reading sessions. For Caitie, who keeps a tarot
journal, and uses tarot to regularly reflect on their life
experience, using ML Tarot felt quite different. It prompted
them to reflect on the tool as much as their identity. They
write, "When I began creating this deck, I felt some anxiety
because I didn't think of myself as a "creative" person. But I
felt as though something possessed me to work on the
deck. I put long hours incessantly tinkering with it,
and sometimes just sitting with it and reflecting on it. Rather
than directly design the cards, I made decisions about which of
the generated texts to use for each of the card descriptions.
Over time, I began to see machine learning as a generative
way for me to explore my creativity, or even recognize
creative potential in myself." Within our reading session,
several researchers noticed themselves initially skeptical
of the decks foundations, only to find themselves leaning into
the process of interpretation--finding AI
lively, lyrical, and poetic. One person explained, "I expected to
be a lot more resistant to the content of the cards (my own
bias against generated content, I guess) but I found it
surprisingly lyrical and relevant... it didn't feel like
it was locking me into meaning, or like there was an
'interpretation' I was after. It was more playful." While this
quality of shifting interpretations is not unique to
machine learning-generated decks, it opens up space for
reflection on different aspects of both oneself and machine
learning. Other researchers felt drawn to the visual material,
where surrealism marked the imagery, finding that AI
prompted emergent visual-textual meanings. One person explained,
"Maybe making your own decks could unlock different futures."
This reader's view reflects the text associated with the card,
The Seven of Cups. The text read, "There are a number of
ways we can relate to this card." In the Seven of Cups, a
paint-like rendering of a still life lives within a room
reminiscent of work by Spanish artist Salvador Dalí, exposing a
certain semantic flexibility. Another person observed, "I
found both the images and the texts to be very interesting. I
enjoyed the dialogues between them and how they allowed me to
triangulate my own meaning." Our experiments have so far
illustrated opportunities for using AI to produce different
modes of reflection using algorithmic systems, exploring
the potential of bringing AI into a creative process of
critique. Just as designers help create explainable AI, might AI
help nurture unexplainable designers? In closing, we
consider how this project uses algorithmic systems to prompt us
as designers to reflect on three aspects of ourselves. In our own
practice and reading session, we first noticed the cards push us
to notice features of our interpretive tools that might be
different--seeing cards of tarot less as tools to design things
or reflect, and more as a means to develop a process or a set of
habits for reflecting on echoes of ourselves. Tapping into our
spiritualities could be generative and speculative,
exposing those echoed selves.
Second, we notice the process helped us reflect on our role as
creators and the way AI/ML asks us to give up some control over
what we create. Perhaps we need to spend more time talking about
humans as curators of algorithmic systems, exposing
how designers make intentional creative choices when they use
AI, exposing our creative selves. Lastly, during our
reading sessions, we realized a series of questions. Was this
reading also a dialogue with the AI, and might (as the designer)
Caitie be acting in some way as a voice for this algorithmic
system? What about our roles and experiences could never be fully
explained? In the case of this ML Tarot deck, interpretability
never fully explained the cards and ineffable aesthetic
experience. But working with the cards makes that ineffability
tangible. The questions expose our ineffable selves. We see
these three aspects of self-knowing as generative
approaches for reflecting on when not to create AI and our
responsibilities to both communities and to ourselves as
designers. If we view these artifacts as manifestations of
inspirited AI, they could be used for inquiry into how
algorithmic systems perceive, explain, and reflect on
themselves. Through slow engagements with tarot, both
alone and together, we illustrate the fraught but also
generative character of algorithmic systems to nurture
diverse practices of self reflection and future-making.
Thank you very much.