Thank you for your interest in our paper, Monsters, Metaphors, and Machine Learning.
My name is Graham Dove and this work was done in collaboration with my colleague at New York University, Anne-Laure Fayard.
The work reported in this paper has been motivated by articles in popular media that apply a
technology-as-monster metaphor to artificial intelligence, and in particular to machine
learning. This is a familiar metaphor, and one which has often been called upon when
new or challenging technologies come into mainstream focus. For example, when genetically
modified organisms, or GMOs, became known as Frankenstein foods in tabloid headlines
at the turn of this century.
We start the paper by identifying examples of this technology-as-monster metaphor.
We then layout some of the challenges that machine learning poses for UX designers,
and provide a background review of the role of metaphor in design research and practice.
Following this we revisit historical human-monster relations, and reintroduce these complex and ambiguous 'others'.
We then discuss how metaphorical monsters can help us understand machine learning.
In the remainder of the paper we show how the technology-as-monster metaphor can be
used generatively when designing with machine learning. We provide detailed examples from
an early-stage design workshop investigating how machine learning might be used to support
student mental health and wellbeing.
In this presentation I focus on two of these sections. First, I will provide an overview
of our reintroduction of the monster figure, and following this I will focus on how we
have used the technology-as-monster metaphor generatively. Each of these will be a brief
discussion, and so I recommend reading the paper, where the narrative is presented in much more depth.
Our view of monsters and the monstrous is rooted in a Western European history, although
we also acknowledge the important influence of other cultural traditions on how this has
developed. In English, the etymology of the word monster typically suggests that it comes
from the Latin for a warning, and in ancient Rome this would mean omens from the gods,
seen in the form of signs, such as still born fetuses with congenital deformities.
More recently, monsters in literature have provided a warning of contemporary concerns.
For example, Mary Shelley’s story ‘Frankenstein or the Modern Prometheus’ tells the tale
of a creature brought to life and then abandoned by its creator. In this story, Victor Frankenstein
brings the creature to life using electricity, which at the time was a powerful new technology
in many ways analogous with computing today.
While monsters instill fear, and act as a warning of possible dangers, their power also
attracts and they can prompt feelings of desire. For example, the Sirens song was so compelling
and attractive that sailors were unable to resist it and would be lured to their death.
Similarly, the desire to achieve miraculous results through magical means leads to monstrous
outcomes in Goethe’s The Sorcerer’s Apprentice and Hasidic Golem tales.
Examples such as Vampires demonstrate how the ‘otherness’ of monsters has been a way to both explore
and demonize desires and practices outside contemporary norms.
The complexity of human-monster relations, the monsters’ strange otherness, and their
facility for introducing the unknown and unfamiliar, have allowed monsters to play an important
role in ritual and rites-of-passage. We exploit this capacity to embody ‘what if?’ questions
when we employ the familiar technology-as-monster metaphor generatively during design activities.
One example of how we use monsters when designing with machine learning, is a set of cards we
have developed for ideation workshops. These cards each show an example of a monster from
popular culture or historical mythology, and draw some connection between the monster’s
story and machine learning. In this way we prompt participants to question their assumptions
or potential design choices.
A second example of how monsters inform our design practice is the workshop format "Where be there monsters?”
In this workshop we bring together different stakeholders, including
domain experts, UX designers and data scientists, to explore the particular challenges of working
with machine learning in very early-stage design. In the paper we discuss this workshop
format in detail, illustrating our discussion with empirical data from an example workshop
inquiring into the possible use of machine learning to support student mental health and wellbeing.
The workshop takes the data that might power a machine learning system,
the ground truth that might describe a particular domain, and the predictions or recommendations a
machine learning system might produce, as key points in a journey map; and then explores the space
around and between these points. We frame this inquiry with a series of worksheets asking
particular questions in the form who, what, why, where, when, and how.
These questions help challenge assumptions and identify uncertainty; and wherever participants find these unknowns
they mark it as a place from which monsters might emerge.
Participants select and develop particular monsters, describing what they represent,
why they should be feared, and what might be done to domesticate them and reduce their
potential to cause damage. For example, in the student mental health and wellbeing workshop
that we discuss in the paper, one group describe a monster they simply called Ground Truth.
This was because the notion of ground truth was so challenging to them, and was the area
where they found so much uncertainty, that they couldn’t answer any of the questions posed
This can be seen in the emptiness of the middle hexagon shown on this slide.
For another group in this workshop, the validity of their selected data sources proved challenging.
This group created a monster called Normy, that challenged their assumptions about how
‘normal’ mental health might be characterized, and how data that might describe it could
be captured. As one member of the group said when explaining their monster.
“Normy shows us there is no normal”.
To sum up. Our research takes the familiar technology-as-monster metaphor as a starting motivation,
and considers how it might be used generatively to support designing with machine learning.
We revisit human-monster relations, and investigate human-machine-learning relations,
in order to draw connections that show how the monsters’ strange and powerful
‘otherness’, which prompts both fear and desire, can help stakeholders embrace the
uncertainty typical in these design situations, and challenge assumptions that might otherwise
only emerge as so-called unforeseen circumstances.
Thank you for your interest in our paper