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