down and pay attention to the dynamics
that would pass by unnoticed—to be
the guardians of meaning making on a
human scale. Given the kinds of sensor
networks we can deploy and the kind
of data we can gather, we now have
many more tools to do this—to see
human behavioral dynamics and the
subtleties of ecological processes. We
are still grasping, however, for reliable
practices that capture and map human
meaning making onto these large
sensor-derived datasets.
The reflexive turn of cybernetics
echoed in the cognitive and social
sciences. Anthropologists started
practicing autoethnography —
systematically revisiting the recorded
traces of their own experience in
order to enrich their understanding
of themselves. Confronting people
with video presentations of their own
activity [ 5] emerged as an explicit
autoconfrontation methodology in the
ethnographic toolkit in the 1990s
and continues to evolve [ 6]. As sensor
networks become ubiquitous and
capture multiple modalities, we are
increasingly surrounded by our own
digital and virtual echoes. Designing
autoconfrontation experiences is already
an important aspect of monitoring
wearable technologies, although we
argue that this methodology could and
should be used throughout the research
and development cycle.
Supporting users’ reflection of
their own doings can be useful during
various phases of the design process and
during the development of technologies
across use contexts. In contrast to
usual recall-based data-collection
methods, self-confrontation offers
two main opportunities: First, the
user is confronted with aspects of the
activity that they do not remember or
did not attend to at the time. Second,
users are able to recall and reflect on
more than is recorded in the data,
providing critical insights for further
design and development. Previous
work has used autoconfrontation in the
development of surgical robotics [ 7]
and intelligent driver-support systems
[ 8]. Autoconfrontation can support
designers in the pre-design phase,
where it supports task analysis and the
discovery phase of user-experience
research, and the validation/calibration
of behavioral metrics phase in simulated
or prototype-testing environments.
More recent work in the context of
systems, which observe themselves, may
adjust their goals. Second-order systems
don’t just react; they may also learn.
When two first-order systems engage,
the result is interaction. They push each
other. When two second-order systems
engage, the result may be conversation,
an exchange about both goals and
means. As discourse on cybernetics
expands to second-order systems, issues
of ethics emerge.
Cybernetics offers a language (both
vocabulary and frameworks) that enable
scientists (and designers and others)
from different domains of knowledge
and practice to communicate—to
describe the structural similarities of
systems and to recognize patterns in
information flows. This shared language
is especially useful in analyzing,
designing, and managing complex,
adaptive systems, which are intertwined
with many of today’s wicked problems.
What designers design. In the past
30 years, design practice has expanded
from a focus on the form of objects to
a broader concern for interaction with
systems and product-service ecologies
(systems of systems).
Today’s products are often smart
(controlled by microprocessors), aware
(full of sensors), and connected (to each
other and to cloud-based services).
These products and services, and
our interactions with them, generate
increasing volumes of data—just when
computer processing is becoming an
on-demand utility and pattern-finding
software (AI) is advancing.
Today’s designers must consider
how information flows through these
systems, how data can make operations
more efficient and user experiences
more meaningful, and how feedback
creates opportunities for learning.
Knowledge of cybernetics can inform
these processes.
How designers design. Traditionally,
designers delivered plans-for-making,
which clients approved before
manufacturing large quantities of
finished things. In mass production,
risks are high (set-up costs, costs of
materials, and costs of fixing mistakes),
causing designers to obsess about
perfecting their plans-for-making.
Designing for systems and product-
service ecologies is different. Today’s
information systems are not mass-
produced. In the language of systems,
they are emergent. They are rarely
defined a priori and in toto; rather, they
grow over time and key features evolve
through interaction with users and the
environment.
Now, instead of finished plans,
designers must create possibilities for
others to design and make; designers
must build flexible platforms, defined
by patterns and rules for interaction and
rules for changing the rules. Instead of
making decisions about what and how,
designers facilitate conversations about
why and who.
In sum, designers are now engaged
in designing first- and second-order
cybernetic systems, and sometimes,
systems for conversation—using
methods that draw on cybernetics.
These changes suggest that
knowledge of cybernetics and other
aspects of systems thinking, such as
systems dynamics and complexity
theory, is a prerequisite for practicing
design going forward.
MIND THE GAP: MAPPING
MEANING MAKING TO SENSOR-DRIVEN DATA IN AI SYSTEMS
By Deborah Forster
The cybernetics movement was
rooted in an appreciation for dynamics
in the natural world. The participants
in the Macy conference series (1946–
1953) were concerned with identifying
biological dynamics that could be
designed and instantiated in man-made
machines and mechanisms (physical,
social, cultural) [ 4]. The participation
of Margaret Mead, Gregory Bateson,
and Heinz von Foerster (as well
as other social scientists) in these
meetings allowed the relevance of the
cybernetics formalisms to seep into less
formalized realms—identified as “soft”
cybernetics—as well as the reflexive
extension to second-order cybernetics.
The more recent success of the
cybernetic turn in technology—
especially technology platforms that can
track and feed back rich sensor input in
iterative cycles—has filtered so quickly
into how we perform our daily lives
that we have missed the opportunity
to reflect properly on the impact of
adaptive technologies. We’ve moved—
almost leaped—from ecological niche
construction to sociotechnical niche
construction. In other words, the
new sociotechnical configurations
are constantly transforming the very
landscape from which they emerge.
In this environment, the task of
user experience practitioners is to slow