ep. 30: On problem (re)framing in design
10 min read
Imagine you’re the new landlord of a building. You’ve received numerous complaints from residents about the building’s slow elevator. You bring in an elevator technician for an inspection. They assure you the elevator is safe, but indeed, slow. The only way to make it faster is to replace the elevator - a costly, lengthy endeavor.
You could assume that the problem to be solved is to getting residents upstairs as fast as possible - most likely, with a new elevator. However, budget is tight, and you want to further investigate the problem before moving forward.
You decide to observe residents while waiting for the elevator. As people wait, they shuffle their feet. Stare at the ceiling. Pull out their phones and aimlessly scroll. You interview some residents. They reveal that they find the wait mind-numbing. They’ve counted all of the flowers on the wallpaper. They’ve studied the elevator doors like an art historian.
The key theme from your research? Waiting for the elevator is boring.
You revisit your initial assumption. Maybe the problem to be solved is to distracting residents while they wait, alleviating boredom. Possible solutions include installing mirrors in the elevator lobby, playing music or installing a fish tank. These options are all significantly cheaper and faster to implement, compared to say, replacing the elevator.
This classic example illustrates the power of problem framing and reframing, critical skills in design. I’ve been thinking a lot about these topics, especially in the context of designing AI solutions. From both experience and academic research on how teams build AI products, teams seek support with problem framing and reframing [1, 2, 3] to help them build a useful AI solution - something that addresses user needs, rather than a technical solution in search of a problem. Existing AI product design guidebooks, such as the People + AI Guidebook, help practitioners “get the design right”. However, there’s an opportunity to offer more guidance on “getting the right design”.
This episode is the first of two parts that will explore what we know about problem framing and reframing, challenges people are facing with problem (re)framing in AI product design, and takeaways for how we can “get the right design”. Today, we examine origins of problem framing and reframing, the reasoning process behind (re)framing, and general takeaways for product design. We’ll get into AI applications next week.
What’s in a frame?
A frame is the way we see things. It defines what we see.
Framing the problem earlier as “people need to get upstairs faster” leads us to see solutions like “replace the elevator”. Framing the problem as “waiting is boring” leads us to see solutions that alleviate boredom.
Framing has origins in sociology, cognitive psychology and communication studies. Understanding these origins can help us understand the ubiquity and importance of framing - helpful scaffolding for understanding framing in design.
Sociology
An important work on framing was written by sociologist Erving Goffman. His 1974 book “Frame Analysis: An Essay on the Organization of Experience" posits that frames are cognitive structures that guide our perception and interpretation, helping us understand and respond to events. Once we have a primary frame, an activity can be given a new interpretation that modifies the original interpretation. This frame transformation is called keying, guided by the context in which an activity occurs, verbal and non-verbal signals (e.g., “let’s pretend” signals a make-believe frame) and social conventions (e.g., rituals and ceremonies often come with predefined frames).
Cognitive psychology
Cognitive psychologists Amos Tversky and Daniel Kahneman researched the framing effect, which demonstrated how different presentations of the same problem can lead to different decisions. For example, if Brand A’s label says, “kills 95% of all germs,” an Brand B’s label says, “only 5% of germs survive”, people are more likely to choose Brand A. The idea of killing germs versus having a few surviving on your counter is more favorable.
Cognitive linguist and philosopher George Lakoff’s work on conceptual metaphors and framing in political discourse showed how language shapes thought and perception. Consider the phrase “tax relief”: When the word “tax” is added to “relief”, the result is a metaphor: Taxation is an affliction. And the person (likely a politician) who takes it away is a hero, and anyone who tries to stop them is bad.
Communication studies
Communications studies scholar Robert Entman defined framing as the process of selecting some aspect of a perceived reality to make them more salient in communication, therefore promoting a particular problem definition, causal interpretation, moral evaluation, and so on. Political scientist Santo Iyengar’s research on framing effects in political communication has shown how different frames can affect public opinion and political behavior.
This interdisciplinary look at framing demonstrates that:
Frames are everywhere: We are constantly engaged in framing, whether we’re conscious of it or not. Similarly, we are constantly seeing things through other’s frames - for instance, when we read about an issue in a news outlet.
Framing impacts our decision-making: How we frame a problem impacts our decision making. The same problem framed in different ways can lead to different decisions.
Reframing transforms our interpretation: Context, language and social conventions can lead us to reframe our initial interpretation.
We’ll now examine how framing manifests in product design, and how you can effectively frame and reframe problems in a human-centered way as you work towards solutions.
Framing in design
Framing is a core design practice. A key feature of design expertise is structuring and formulating the design problem in a creative, novel way. We have an understanding of the reasoning patterns expert practitioners use to frame and reframe problems, thanks to research by scholars like design professor Kees Dorst.
The core reasoning equation we tend to use in product development is:
“What” is an object, service, or system. “How” is a known working principle that will help achieve the Value we aim to deliver to customers. The What and How together should achieve this value proposition for the customer.
We commonly see two types of abductive reasoning (making a probable conclusion from what you know) challenges in design. The first is when we know the value we want to create, and the “how’". We are missing the “what”. Designers and engineers regularly engage in this form of reasoning. It looks like:
The second form of abduction is more complex. At the start of the problem solving process, we only know the end value we want to achieve. It looks like:
The challenge is “what” to create, while there is no known or chosen “working principle” we can trust to lead to the aspired value. We need to create both the thing and working principle in parallel.
People just learning design tend to generate nearly random proposals for “what” and “how”, seeking to find a matching pair that leads to value. Experienced practitioners develop or adopt a frame: the implication that by applying a certain “how” (aka working principle), we will create a specific value.
In other words, IF we look at the problem from a certain perspective, and adopt the working principle associated with that perspective, THEN we will create the aspired value.
The most logical way to approach solving for the two sets of “???” is working backwards from value ( a type of induction) to devise the “how”, and design a corresponding “what” (e.g., a prototype of the object, system, service). Then you can use deduction to see if “what” plus “how” perform well enough together to create value. While design leverages many different reasoning approaches, this emphasis on complex abduction and frame creation is a core specialty of design practice.
Mapping to the elevator example
Experienced practitioners engage with a novel problem situation by searching for the central paradox, asking themselves what it is that makes the problem so hard to solve. They only start working toward a solution once the nature of the core paradox has been established to their satisfaction. Practitioners tend to focus on issues around the paradox first, searching the broader problem space for clues. Reading a complex situation in terms of themes is a key way to engage with problem space, leading to the emergence of new frames.
In our elevator example, we had:
Initial frame: get upstairs as fast as possible (“how”) to save residents time (“value”)
Design research: observations, interviews with residents, which treated the initial frame as a testable hypothesis. Your evidence didn’t support that hypothesis: people weren’t rushing to conduct life-saving activities in their apartments, but were instead, simply bored.
Reframe based on evidence: Make the wait more interesting (“how”) to alleviate boredom (“value”). You are now free to ideate on the “what” - for example, mirrors, music and fishtanks. You can later test the hypothesis that “what” and “how” together deliver on the value proposition (e.g., does the mirror make the wait more interesting?) We can use a prototype, or study analogs, in cases of more complex “whats”.
Takeaways
This deep-dive on framing and reframing offers learnings about how we can be more mindful of the frames we adopt, and how to challenge entrenched frames (aka assumptions) to come up with solutions that deliver customer value:
Examine your current frame: What is the value you assume your customers seek (e.g., “save time”)? What assumptions are you making about “how” that value is realized (e.g., “get upstairs as fast as possible”)? What evidence do you have to support these assumptions?
Treat your initial frame as a hypothesis: It’s ok to have an assumption. You need to start somewhere. What is important is that you test that assumption - treat your initial frame as a hypothesis. In the elevator example, the initial hypothesis to be tested was: “People want to get upstairs as fast as possible to save time”.
Test your hypothesis: Learn from your target audience. There are design research methods for effectively learning from your target audience, and sense-making from the resulting data. A poorly constructed study is garbage-in, garbage out. Surface-level synthesis will not yield the non-obvious insights you need to push your thinking forward. Use this framework as a starting point, and ideally, partner with a design researcher.
Reframe based on evidence: If evidence did not support your hypothesis, revise your value proposition accordingly. From here, you can start to build out the “how” (i.e., “make the wait more interesting”) to complete your new frame. When we hear people say, “frame this from the user’s perspective”, this is what they’re talking about.
Ideate on the “WHAT”: What solutions (e.g., mirror) might lead your “how” (make the wait more interesting) to deliver on your value proposition (alleviate boredom)? From here, we get into criteria for selecting ideation, and subsequent prototype testing - topics that play out over weeks when I teach them, so out of scope for this summary.
Next week, we’ll examine how problem framing and reframing plays out in AI product design, and why it remains a particularly challenging task for practitioners (hint: it’s related to starting the framing process with “WHAT”).
Upcoming event
Sendfull founder Stef Hutka presents on Embodied, by Design at Kinetech Arts: Y-Exchange May 29th 7-8:30pm at ODC Dance Commons. Movement artist Gizeh Muñiz Vengel will also be performing. RSVP here.
Human-Computer Interaction News
Apple announces new accessibility features, including Eye Tracking, Music Haptics, and Vocal Shortcuts: One particularly interesting feature at the intersection of design and neuroscience is Vehicle Motion Cues, which help reduce motion sickness when using your device in a moving vehicle. Animated dots on the edges of the screen represent changes in vehicle motion, helping reduce sensory conflict (a disconnect between what a person sees and what they feel) without interfering with the main content. Read more about the science behind the feature here.
Building a better sarcasm detector: Researchers at the University of Groningen have developed a multimodal algorithm for improved sarcasm detection that examines multiple aspects of audio recordings for increased accuracy. They used two complementary approaches, sentiment analysis using text and emotion recognition using audio, for a more complete picture. This is newsworthy because sarcasm is notoriously difficult to convey through text, and the subtle changes in tone that convey sarcasm often confuse computer algorithms as well, limiting virtual assistants and content analysis tools. Read more here.
Robotic palm mimics human touch: MIT researchers have developed a robotic hand, GelPalm, which mimics the human touch using advanced tactile sensors embedded in its palm and flexible fingers. This innovation enhances the robot's ability to handle objects with precision and delicacy, improving human-robot interaction and prosthetic technology. The palm features a gel-based sensor with RGB LED illumination and a camera to create detailed 3D surface models. The fingers have something called “passive compliance,” which is when a robot can adjust to forces naturally, without needing motors or extra control.
Interested in new ways to frame the problems you’re solving? Sendfull can help. Reach out at hello@sendfull.com
That’s a wrap 🌯 . More human-computer interaction news from Sendfull next week.