Removing bias from your solutions

Indi Young
Jan 1, 2021 · 25 min read

Help more humans feel heard and supported

With the pressure of the pandemic and quarantine, it has become increasingly clear just how globally bias, discrimination, and systemic racism are rooted in our lives. People do not have equal access to services, online learning, clean air and water, and justice. From governments to societies, business to technology, we who help design solutions are making decisions quickly, blindly, and dangerously. Those decisions affect people in real, often harmful, ways.

So many books and papers give evidence to this.¹

One powerful example of systemic bias built into a digital product is the recent story of facial recognition software that an MIT study, led by researcher Joy Buolamwini, found to be significantly more error-prone when it was working with darker skin and/or female faces. The three programs they tested, which attempted to determine gender (on a binary male/female basis) via facial recognition, had an error rate of 0.8% with light-skinned males, whereas the error rate shot up as high as 34% for dark-skinned females. In June of 2020, facial recognition became news because of Joy’s work with her team to publicize these results. Both IBM and Microsoft both announced an end to their facial recognition software development programs. In Boston USA, the City Council voted to ban facial recognition. This example shows that we can make changes. We can pay better attention to the damage our technology causes, and we can direct our work toward more aware, careful, and supportive paths.

All the decades of my career I’ve been tilting at the windmill of tech’s process of speeding along to launch. While the practitioners may be aware of the quandary, the orgs as entities worship speed and enjoy simplifying to the common conceptual representation. Orgs erroneously believe that the messiness of true human thinking, and inconsistency of moods, needs to be simplified to affect their market share. Simplification of humanity down to surface-level archetypes leads directly to assumptions, to bias, and from there to harmful experiences and outright discrimination.

This pandemic is a time for change.

Our Tools for Change

As researchers, designers, and product managers, we may or may not be in positions of power within our organizations, but we do have tools for change. And we do have a position close to the design of a product. Because of that position, we have the ability to influence our team’s thinking.

  1. Explore how to ask for knowledge
  2. Deepen your understanding of people
  3. Seek the bias, discrimination, and weaknesses causing harm
  4. Slow down, take time

As the pandemic unfolded, many people wanted to help. These people knew they were in relatively privileged positions, not vulnerable to the most painful effects of job loss, declining health, or demanding landlords. So people looked around for others and formed groups to do something that might make a positive difference. And that’s just it. We were eager to fix things and make progress without first understanding the problem deeply. We wanted a problem title, a short description, and then to roll up our sleeves and get to work creating solutions. This is how we’ve been raised. From a tender age, most of us are judged based on what we have learned and on our ability to apply that knowledge to solve a problem. At any level we make our human way through life, work, institutions, schools, and universities learning how to be problem solvers. Our movies and games and idols are all about people solving things. We’re very enthusiastic about coming up with solutions.

Unfortunately, spending time understanding a problem first before coming up with ideas takes a back seat. But, understanding a problem first is how we stop ourselves from making assumptions and causing harm or discrimination.

Passionate practitioners are not getting a nutritious, complete education about the work of design & research. The discipline of creating digital products and services has not been around that long. Because of demand, the number of people working in User Experience (UX) and Product Management (PM) roles is growing rapidly. These people are passionate about helping others, about making things better than how they are. In parallel with their workload, they are consuming every bit of learning about the discipline that they can find, and often those lessons are incomplete or perpetuate unconscious or cultural biases. (I’m guilty of this, too.) Exciting methods that promise to lighten the load of research or design work come along but fail to explain how the labor of knowledge-creation is done.

Team members are following a set of steps they assume are correct because everybody else is doing them, and they never hear of anything else. In parallel, stakeholders & leadership coming from business education have been introduced to a limited set of tools for creating knowledge. Often they aren’t aware that the surveys and A/B tests they ask for are one of many, many methods and philosophies of knowledge creation.

Tech (coding) and business love simplifying/purifying down to the common concepts. Here is the foundation of the quandary. The tech industry, including programming, designing, and testing, has a focus on taking complex and complicated concepts and making them simple to interact with. It’s a point of pride to purify things down to their base components, and to be able to build new things from those components. However, the complicated complexity of human behavior is a bit too much to model. Ever. (You can seek more ways to support patterns you find, but you can’t completely model it.) Real humans have their own messy frames of reference that change, and they have agency and emotions that influence inner thinking.

The first antidote is to remind ourselves we don’t know everything. We know what we have experienced; we know what we have read. Our teams pool this knowledge to come up with ideas, or design based on a very small set of “subject matter experts.” But we are human and often forget that there are other perspectives out there, other approaches. We need some sort of constant reminder about other people’s approaches and thinking styles. (Thinking styles are explained in Section 3.)

Here’s a story that illustrates the impact of these setbacks — and some alternative approaches to address this. I was working with a design team at an airline. The airline was experiencing low on-time departure metrics. The stakeholders assumed that if they reduced the number of carry-on bags, people would get settled in their seats faster, and then on-time departure metrics would rise. These stakeholders also assumed that on-time departure was a metric that was important to the different thinking-styles of passengers. But the stakeholders weren’t aware that they held these assumptions.

The stakeholders tasked the design team with designing a way to get more people to check their luggage rather than carry it on. So the team decided to begin with research. They asked other airline teams for existing data about checking bags. They received quantitative data sets, such as this one.

Average per day: 332,982 passengers who checked 209,548 bags. Of these passengers, 137,981 were female, 71,567 were male. Age
Average per day: 332,982 passengers who checked 209,548 bags. Of these passengers, 137,981 were female, 71,567 were male. Age

On Day X: 332,982 passengers who checked 209,548 bags:

  • 137,981 were female (66%)
  • 71,567 were male (34%)
  • Age range 65–75 checked 8,842 bags
  • Age range 45–64 checked 63,890 bags
  • Age range 35–44 checked 78,098 bags
  • Age range 25–34 checked 55,652 bags
  • 56,309 of the bags were checked at curbside
  • 98,993 of the bags were checked at the kiosks inside the airports
  • 54,246 of the bags were checked at the counter by an agent

I had been working with this team for several months already, helping them connect with better awareness of their design decisions. When they received this quantitative data about checked baggage, I immediately noticed the way the data was broken down. Somewhere along the line, the other airline team supplying this data made decisions about what information was considered important and relevant, but I failed to see the connection to passengers’ real thinking. I asked the team: what’s wrong with this picture?

They considered what they were seeing and responded, “The data set is broken down in a conventional way: gender, age, etc. The gender they show is only binary. The info is only drawn from ticket data. This breakdown may not have anything to do with checking a bag.” Good! I asked them what they would do in place of this. Here’s what I got back.

Average per day: 332,982 passengers who checked 209,548 bags. Of these passengers, 137,981 had a connecting flight, and 71,56
Average per day: 332,982 passengers who checked 209,548 bags. Of these passengers, 137,981 had a connecting flight, and 71,56

On Day X: 332,982 passengers who checked 209,548 bags

  • 137,981 had a connecting flight
  • 71,567 had direct flights
  • 8,842 of the bags were from passengers with a 7+ day trip
  • 63,890 of the bags were from passengers with a 4–6 day trip
  • 78,098 of the bags were from passengers with a 2–3 day trip
  • 55,652 of the bags were from passengers with an overnight trip
  • 56,309 of the bags were checked on the way to the destination
  • 98,993 of the bags were checked on the return home
  • 54,246 of the bags were checked in the middle of a multi-city trip

A member of the team told me, “When I have a connection, I’m less likely to want to drag a roll-aboard along with me through the airport. And when I take a longer trip, I pack more, therefore I’m likely to need a bigger suitcase and check it in. Also, I’m more likely to check my bag on my return home because if it gets lost, I still have clothes at home to wear. I don’t have to go buy replacements like I would if my bags were lost on the way to my destination.” You may be thinking these explanations don’t match the data, but it’s trickier than that. Notice all the “I’s” in there. It only represents their own experiences and thinking. This is research theater.

Instead, I helped the team define a study to understand what went through people’s minds the last few times they took bags to the airport. We did listening sessions to carefully collect other perspectives. It took us a month (10 hours a week, five researchers) to collect and analyze this qualitative data. We used a two-step analysis method rather than risk curating the patterns according to our own unconscious bias. And guess what? The results had nothing to do with gender or age, nor did they have anything to do with connections, the length of the trip, or whether a person was going out or coming back. Here are the four thinking-style patterns we found.

Thinking styles discovered so far: 1. Never Again, 2. My Precious, 3. Had To, 4. Hassle Drag
Thinking styles discovered so far: 1. Never Again, 2. My Precious, 3. Had To, 4. Hassle Drag

Thinking styles discovered so far:

“Never Again:” I carried on my bag because I worry I will lose a checked bag; I assume my checked bag will be thrashed again; I wonder what the white powder was on my bag.

“My Precious:” I carried on my bag because I keep my expensive medical device with me; I make sure my guitar is not damaged in baggage; I carry on my computer & camera so they are not stolen.

“Had To:” I checked my bag because I check things I can’t get through security, like beer from Belgium; I check my bulky gift, my poster, my scuba gear that won’t fit in the overhead bin.

“Hassle Drag:” I check my bag because I avoid rolling the bag through connecting airports; I need to keep track of my kids, not my roll-aboard; I realize picking up the checked bag only adds 5 minutes.

This work gave us deeper insight into what was truly happening. It also reminded us that the approaches were contextually dependent, so any one person may change their thinking style regarding bags from flight to flight. That’s an important point when it comes to creating baggage services that vary in tone and support for the various thinking styles.

For example, the airline team might come up with a set of stories called “All the Amazing Precious Things We’ve Carried in Our Overhead Bins.” These stories would be interest pieces about some fabulous real stories. Over time, they might help readers associate the overhead bins with precious items. Another example might deal with lost baggage Or the team might create a new service for lost baggage that helps passengers avoid the Never Again mindset. The service would find stores close to where a passenger is staying to buy replacement items with pre-arranged store vouchers. In foreign cities, the service could even provide videos made in the local language to help passengers interact with store clerks to use the voucher and obtain items. They could provide a personal buyer service for people who have no time in their schedules to visit stores. (These ideas are thanks to teams in my online course.)

It’s time to stop thinking in terms of making one experience that will fit everyone. Here’s where we can use machine learning to pick up on a person’s current thinking-style and curve the experience into a shape that better supports that mindset. This doesn’t mean bespoke experiences for every human, but different experiences for the different thinking-styles. The thinking-styles were discovered and verified through iterative ‘problem space research’ (defined below). They are reliable. The airline can create four different experiences for thinking about packing. They can design four different ways to help people decide about baggage upon entering the airport. Any one human might be a “My Precious” on one flight, bringing a family heirloom to a newly married couple, and a “Hassle Drag” on the way home.

This is how our discipline can veer away from the myth of the “average user.”

Problem space research

The airline study that I conducted with the team is all about understanding different perspectives by studying three things that go through people’s minds at depth:

  • Inner thinking
  • Emotional reactions
  • Guiding principles

It uses listening sessions to develop cognitive empathy. We keep adding knowledge over time to get better understanding so we can support different thinking-styles and see perspectives that we missed. You can build knowledge like this in both the problem space and in the solution space. When you build this knowledge in the problem space, it feeds your organization’s strategy. Without it, you’re making decisions based on your own team’s bounded thinking and experiences. Here’s a diagram how the problem space feeds strategy, and how that drives your typical solution space ideation and development processes.

Problem space → strategy → solution space

One thing to note in the diagram above is the words “person” versus “user.” In the solution space, “user” refers to someone who has a relationship to your organization — or a potential relationship. You are looking at this person through the lens of your organization, your solution, or your service. Yes, you might prefer to call them “passengers” (or “customers” or “guests” or “members” or any of the other nouns that we like to use), but they still are seen in relationship to the organization.

In the problem space, your organization isn’t part of the picture. You study people as people, trying to accomplish a purpose that can be achieved in a number of different ways.

For example, if your cat has cancer of the tongue and is having trouble eating, you are desperate to get nutrition into him; you are desperate to make him comfortable in the grip of such an awful disease. Part of your thinking might contemplate getting a can of tuna and feeding him the juice from it, or remember that he loves condensed milk, and feeding him that. If you look at this person through the lens of a pet store, you might constrain your thinking by the products and services you already sell. If you look at this person through the lens of a grocery store, these purposes for canned tuna and condensed milk may never enter your mind. There are opportunities here for both organizations, should they choose to seek deeper understanding of people in the problem space.

We don’t know everything. This is the curious mindset.

6 quadrants: Quant & Qual along y-axis, Opportunity, Generative, Evaluative along x-axis, latter 2 part of solution space
6 quadrants: Quant & Qual along y-axis, Opportunity, Generative, Evaluative along x-axis, latter 2 part of solution space
Research = knowledge creation, yet most orgs focus only on creating knowledge about people through the lens of their existing products and services.

2. Deepen Your Understanding of People

Once you realize there is a difference between people and users, between the problem space and the solution space, you can start the work of gathering knowledge about people in the problem space. How? First of all, this may require persuasion. It definitely requires relationship-building.

We face three setbacks:

Under pressure, we create solutions that graze the surface of the problem, instead of helping people achieve their purposes according to their own thinking-styles. Our discipline is also heavily influenced by the need for speed. Researchers get pushed to answer deep questions in a short period of time. UX and PM practitioners are expected to automate their understanding of humans through big data and guesses based on demographic statistics. Business culture is built around minimum viable products and advertise-sell-engage goals. Business schools teach that statistics are objective; technology culture insists algorithms are neutral. All of this is fantasy that doesn’t work well to support real humans in their real contexts.

Because there were a few days of “understanding people” in the process, practitioners feel reassured that they did it right. There are numerous design methods that put understanding of people into one neat step: JTBD, Design Thinking, etc. In reality this step gets shortened down to a day or two, then rarely gets revisited and expanded over time. Teams focus instead on the fun work of coming up with ideas that will help…in other words, solving problems.

The mountains of data remain unconquered: The mirror image of the above is this: teams that do collect a lot of data, both quantitative and qualitative. In this case, the mountain of data is overwhelming; just the thought of synthesizing all of it induces cognitive paralysis. Analysis and synthesis keep getting postponed. So teams seek automated shortcuts to convert all those words into usable insights. Research analysis automation looks for keywords (rather than untangling complicated concepts) and curates insights based on the same understanding the team started out with. The depth and new perspectives are lost.

You, in your own role, are the person to start the process to make sure deep understanding is part of your organization’s process. If you are laughing, consider this next paragraph. You really can start this if you want to.

Listening Sessions

Start by doing your own deep understanding of your boss, their boss, and others at those levels. This means short one-on-one listening sessions with each of these people. Ask, “what went through your mind” about a project or decision or time period. By repeated listening sessions with each person, you can pick up their vocabulary and perspective. And most importantly, you can forge trust. Listening sessions with your stakeholders build trust, so you can collaborate more fluidly. You can understand their inner thinking, use their vocabulary, speak to their concerns, and eventually help them see the value of doing problem space research. Listening sessions with your peers build working relationships, because you can see from their perspectives.

Anyone who has experienced someone listening to them intently, without reminders of judgment, persuasion, or power hierarchy, starts to feel heard. Making someone feel heard is a way to build trust.

You may have already practiced this if you’ve done active listening or non-directed interviews. It’s called empathic listening in the psychology field, or even referred to by the nickname “empathy.” To call attention to the fact that there is no list of questions, I call it a listening session.

Sometimes you do a listening session in response to something you sense in a person you work with. Other times you do a listening session with people you carefully recruit who have done a lot of thinking about a particular purpose. In the first case, you use a technique called “empathic listening” to recognize the situation and turn it into a listening session.

1. Recognize that a person has something going on

  • A person might pause because they are having an inner monologue, considering things, debating the principles at stake, or experiencing an emotional reaction, which might be setting off inner thinking or more emotions. Or this might happen without a pause, carried out verbally or in action.
  • Practice noticing when something is going on for someone. This isn’t something everyone’s good at — we all miss cues. Practice paying attention.

2. Recognize that this something is their truth

  • Every person has a history. What is going on for them now stems from their experiences. It’s not something they invented; it’s a direct line from their past to the present. Give it respect.
  • If your thoughts turn to reassurances or judgment, stop your tongue. Avoid “don’t worry” or “you should try” statements. Their worry, their thinking, their inner conflict is completely valid in their mind.

3. Communicate your recognition of their something

  • Your recognition reassures this person that you respect that something’s going on and that you aren’t going to try to fix it.
  • Communicating your recognition is an invitation for this person to share what’s going on. Your communication could be like this:
    i. “I can see that you’ve
    ii. “It seemed like you had some thinking around the presentation earlier, what went through your mind?”
  • This person may not take you up on the invitation, and that’s okay.
    i. Depending on what you sense, you might try again another time. Trust is typically built over time, as you repeatedly show genuine interest and lack of judgment

4. Stay out of judgment as you listen

  • Here is the start of a listening session. Here is where you practice your skills of supporting another person’s reasoning and reactions as they tell you what’s been going through their mind.
  • Help this person get past the surface of explanations, preferences, opinions, and statements of fact so that you can dive into what’s beneath these: inner thinking, emotional reactions, and guiding principles.
  • Develop a safe space for this person to speak their truth to you.

Listening sessions are about a person’s purpose. You aim to understand a person at depth, their inner thinking, emotional reactions, and guiding principles, as they make progress on achieving their purpose.

Listening sessions take skill. But they have a lower barrier for those of us who are not super outgoing. Here’s why: listening sessions are always done one-on one. And for research studies, they are always done remotely, by audio only. This makes them perfect for the pandemic. For many reasons, two people can connect more deeply when they don’t have to face one another. And since we are exploring a person’s purpose, not their relationship to a product or service, there’s usually no reason to see anything they are doing. We’re interested in what’s going through their mind, so we can develop cognitive empathy. The best way to get good at empathic listening is to take a course or read a book, and then practice it as much as possible. Practice empathic listening for your coworkers, neighbors, and with strangers you meet while waiting for something.²

To understand people at depth, you use listening sessions . You build trust with a person and dive past the surface explanations, preferences, and opinions to the underlying triad:

● Inner thinking

● Emotional reactions

● Guiding principles

Cognitive empathy is exactly this triad, above. Cognitive empathy is the type of empathy that I constantly use in my work. It is developed over time through many listening sessions.

Cognitive empathy is extremely important for rooting out discrimination, because you can begin to see your own assumptions from other people’s perspective.

When I worked with the team at the airline, the VP of that group already understood the value of problem space research. It was a lucky situation. For the airline, we conducted 8 different studies over 18 months. Collectively we listened to 100 passengers at depth to hear their inner thinking as they pursued different purposes.

Except the situation didn’t last. The airline got bought by another airline, and the VP’s new leadership had little interest in passengers. The new leadership were interested in keeping the planes safe from potential crashes … and in reducing complaints, keeping passengers loyal by offering perks to those who qualified, selling more tickets and packages to offset their costs. These are all good goals for the airline, but they fall so short of people’s purposes. The purchasing airline had a strong marketing division that understood passengers by the amount of money they spent on tickets, but nothing else. In a year, the VP left the company, followed by most of the team members. Those who remain with the airline have repeatedly referenced the deep understanding as they polish existing services, but have been unable to change the strategy at the purchasing airline.

3. Seek the Bias, Discrimination, and Weakness Causing Harm

As I mentioned in the beginning of this essay, there are layers of harm that we’re unconsciously aiming at the people we mean to support.³

Our teams represent the experiences of everyone on the team. That means the more diverse your team is, the better you’ll be at seeing different perspectives. However, you must also do research, because that is how you capture a greater breadth of experiences beyond those represented by your team. Your team members, however diverse, still only represent their own experiences.

We face two setbacks:

Our discipline is at fault for ignoring ethics. Other disciplines make sure practitioners have a thorough foundation of ethics before doing professional work.

We entirely miss gaps between their approaches and our solutions. Because of the setbacks listed earlier in this essay, our discipline satisfies itself with a surface-level understanding of people. We focus on explanations, preferences, opinions, and generalizations. We lack the depth required to form cognitive empathy and walk in others’ shoes.

Think of the words “different users” when in the solution space. Go understand the problem space once a year or so. And focus on researching and extending support to new thinking-styles and new approaches each year. Over time your organization will intentionally, systematically seek the thinking, emotions, and guiding principles that have been ignored or that have developed as a result of harm and discrimination.

We need a way to collect how different people approach the purposes that our solutions support. We need a way to depict and sustain a picture of the diversity of thought that is applied to the purposes. We need a map to see where our organization falls short, and a way to measure how we are getting better at supporting different approaches. I use an opportunity map, which is your current products and services aligned to a mental model diagram, with thinking-styles and metrics layered on top. This is a map that teams add to over the years; it lasts for decades.

The key to creating this map is to frame your studies by different purposes people seek to achieve, where they may (or may not) reach out to the solutions your org creates, alongside other solutions that include manual, digital, mechanical, social, and cognitive tools.

Across those eight studies that we got to conduct while the VP was in control of the group at the airline, we studied six different purposes:

● Decide whether to take a trip

● Figure out how to best get to a destination

● Get ready for a trip

● Get to the departure area on time

● Undergo the travel itself

● Arrive at my destination

Notice these purposes could be fulfilled using other services besides an airline. That hallmark is how you frame a study in the problem space; the organization fades from view and the purpose becomes the priority. We studied these purposes with several different thinking-styles in mind, as well as some priority demographic segments.

Mental Model Diagrams as Opportunity Maps

We collected all the data together in a mental model diagram, and we looked at the data to derive thinking styles. We layered the thinking styles and other data, such as mileage program membership and ticket prices, on top of the mental model diagram. Then we aligned how the airline currently serves people beneath the mental model diagram. What resulted was an opportunity map with several clear gaps.

I can’t share the airline opportunity map, but I can share a similar set of data collected for the organization responsible for helping employers in the US adhere to laws about hiring people with disabilities. It looks like a city skyline with towers of different heights. The blocks on the lower level represent the organization’s support for people’s purposes, the organization’s capabilities. Each block is a feature or service that supports the thinking style above it. In one or two places these foundations go deep. In other places, they don’t exist or are quite weak.

This opportunity map is available at https://indiyoung.com//wp-content/medialib/pdfs/AskEARN_MentalModel_1.08.pdf

Each of the little blocks in the towers on the top half is a concept: a piece of inner thinking, an emotional reaction, or a guiding principle. Each concept is a summary of what one or more research participants expressed in a listening session.

In the airline study there was a section that was about being away from home. There was a tower in that section called Stay In touch with loved ones who are not with me. It included these concepts:

● Communicate with my girlfriend daily to stay in touch, since we are apart from one another, even if it’s just via Facebook and not a phone call (guiding principle)

● Decide to purchase WiFi for leisure email (rather than work email) and for catching up on Facebook (inner thinking)

● Lean against the window for an extra bar of connectivity so my parents can hear me when I call them from the lounge in SFO (inner thinking)

● Feel amused sharing my experience of the man yelling in the terminal in an email to my friend who had said I shouldn’t expect any yelling, even though it happens on every trip I make (emotional reaction)

● Accept that I will be without a phone on the trip when the charger I bought doesn’t work because I figure that I spent years without a phone before, so I can get by without one for now (inner thinking)

Here are some example concepts from the airline map in a tower called Pause to think about it before buying:

● Pause before buying anything, whether it’s $3 or $500, to ask myself if this is the right thing to buy, to be thoughtful about how I spend my money (guiding principle)

● Question whether 6 upgrades enough to make it worth staying in the mileage program (inner thinking)

● Feel appreciative of the extra time I get to decide whether to renew because my membership was extended for a month (emotional reaction)

● Feel comfortable when I have the control to hold and think about a purchase of a few hundred bucks for a few hours before committing to it (emotional reaction)

You can see the depth and variety of thinking, emotion, and guiding principles here. We don’t know what the towers in the city skyline will be until we compare each summary to other summaries to see what has affinity.

If the airline’s new management had been open to this knowledge, the team would have started prioritizing the gaps in the opportunity map by thinking-style. They would have planned out a year or two of projects to make headway against those gaps, and they would have put in place a way of measuring how well the new services support a tower in this map — how well the services support different thinking-styles within each tower addressed. Over time, the team would have filled in these gaps and made sure the metrics were trending toward support.

I’ve mentioned thinking-styles a lot, and you have an idea that they are archetypes, sort of similar to personas. Thinking Styles are deeply researched, demographics-free mindsets. They are archetypes with no faces or personal names, but titles like these:

Example airline thinking-styles (I can’t share them all):

Get It Over With: I want to accomplish everything, so I set up a tight schedule on my travel day. I have appointments or events I want to hit before and/or after the flight. Or maybe I’m uncomfortable on planes. Or I don’t want to spend too much time away from my home and family. I’m prepared to bury myself in my work or another distraction while on board.

Unfazed: I want my travel to be stress free and drama free. I’ve arranged contingencies for all the scenarios I can imagine. Long security line? I show up an extra hour early. Flight delays? I brought stuff to do. Food? I bring something on board.

I use thinking styles for a few things. Mainly they’re a way to measure how well our support addresses different philosophical approaches to the purpose. (No org measures this well, yet.) They are also used for the characters who play roles in an org’s design scenarios, where any character can switch thinking-styles based on context. Each scenario has different through-lines for the different thinking-styles — different ways of interacting, tone of voice, and expected responses.

You may have noticed that each concept in the towers of the opportunity map has a color. These colors represent the thinking-style of the participants represented. You can use other graphic elements besides color to indicate a thinking-style. Graphic representation gets even more impactful when it starts representing cases where demographics or physiology cause thinking, emotion, or the formation of guiding principles. This helps your team pinpoint the areas where harmful experiences are likely if the support is not designed with these cases in mind. This way the potential gaps and harm can be easily seen across the diagram as a whole. The organization has a clear path to remedy these heretofore ignored cases.

Just seeing the opportunity map laid out, with the mental model diagram above and the org’s solutions aligned below, goes a long way toward breaking teams out of their assumptions.

4. Slow Down, Take Time

Problem space research is eye-opening, freeing, and confidence-building for teams. However, teams have encountered stakeholders who don’t understand the value of working outside of the solution space. It does not fit into any of the design methods that are popular. But the pandemic has forced us to work together differently. It has forced us into a different view of time. As practitioners, we are in positions that allow us to open the door now to more careful, thoughtful research. Time is the key factor — or how you think about time.

Problem space research is something you do once a year or so. The data lasts for decades. The resulting opportunity map, thinking styles, and metrics influence every cycle of the solution space. You can pass the opportunity map on to the next generation of product designers at your organization. These are longer time frames than we are used to thinking in.

I believe the pandemic is, was, the perfect time to introduce this long-view kind of work. Now is the time to build a consistent practice of knowledge-creation about how people actually think on their own, without the lens of your solutions. We are faced with other long-term thinking with respect to other problems with our planet. We find ourselves forced into the era of long-view work.

It takes time to form cognitive empathy, and it takes time to do the right research to get a broad range of perspectives. One powerful way to talk about this necessary time is to re-frame the “fast vs slow” conversation as “slow vs hasty.” Hasty implies that mistakes will be made. Mistakes come from letting bias and assumptions guide decisions. Making mistakes with respect to supporting people means causing harm. That’s just not ethical.

While we may or may not have power within our organizations, we do have influence. We can find ways to recognize our own, and others, assumptions, bias, discrimination, and racism as they happen — and call attention to them. We can talk about the importance of taking time to understand people with our teams and stakeholders. We can start actively removing the bias from our products right now.

I have a list of further recommended reading on my website for those who wish to dive deeper into the topics in this essay.

¹ Race After Technology by Ruha Benjamin; Weapons of Math Destruction by Cathy O’Neil; Technically Wrong by Sara Wachter-Boettcher; Mismatch by Kat Holmes; Future Ethics by Cennydd Bowles; Ruined by Design by Mike Montiero, Design for Real Life by Sara Wachter-Boettcher and Eric Meyer.

² I wrote and narrated Practical Empathy to help practitioners learn listening skills, and I teach an advanced course called Listening Deeply. (The course is available in Spanish, Curso de Escucha Profundo by Bibiana Nunes, in Chinese at UXOffer by Yushi Wang, and soon in Portugues by Amyris Fernandez.) There is a “layperson” book written by a psychology professor William Miller called Listening Well which is also a good resource.

³ Yes, some orgs purposefully manipulate people, and that’s a whole different philosophy that I don’t share. So I won’t address the ethos of those orgs here; participate only if you are fine with manipulating or being manipulated.

User Research Explained

A Charity Collection Of Essays For Pandemic Relief (To Benefit Doctors Without Borders)

User Research Explained

User Research Explained is an international effort to compile best practices and perspectives on effective user research. Edited by Adrian Murphy, Jo Herlihy, Bobby King, and Lisa Galarneau Ph.D

Indi Young

Written by

Freelance problem space researcher helping digital clients find opportunities to support diversity; author & speaker; www.indiyoung.com; cofounder Adaptive Path

User Research Explained

User Research Explained is an international effort to compile best practices and perspectives on effective user research. Edited by Adrian Murphy, Jo Herlihy, Bobby King, and Lisa Galarneau Ph.D