Shift to Product Inclusion
After completing this unit, you’ll be able to:
- Explain the difference between designing with and designing for.
- Discuss why there is no “average” user.
- Describe the persona spectrum.
With and For
A design doesn’t exist in a vacuum. It’s shaped by technical and business requirements, of course. It’s also shaped by a history of events and decisions. Stop and think about that for a moment. We may or may not be aware of the history and the chain of decisions that ultimately determined the design of the final product or solution. What this means is that inclusive design is not only about designing solutions in new ways. It’s about disrupting a (very) long pattern of exclusion habits.
In how we practice inclusive design, one of the exclusion habits we need to break is designing for a particular audience based on assumptions we make about that group. If, instead, we codesign with experts from that group, their insights will help yield a better product. But we have to be intentional about how we open up the process, so that those who are most impacted by a product or solution get to contribute in a meaningful way to the design.
There’s No Such Thing As Average
Human diversity is hard to quantify. But to design a product means having to narrow down the attributes of the range of potential users. Designers often think about who might be the “average” or “normal” user. Just collect some data and average it, right? Not exactly. But basing decisions on an average human is a common–and exclusionary–practice that stems from the work of a 19th-century Belgian astronomer and mathematician named Adolphe Quetelet. He used the Gaussian distribution–also known as the bell curve–as his guiding principle to collect and plot data about human bodies (height-to-weight ratios and rates of growth).
Image Description: Image of a bell curve, with the center representing the distribution of “most people” and the apex representing the “average person.” Plotting humans on a bell curve suggests there’s an “average” human, but there is no such thing as average.
Quetelet discovered that his data about human attributes mapped to the bell curve. He declared the center of the curve the “perfect man” and became obsessed about finding out what traits–perfect face, height, intelligence, moral character, and others–constitute the ideal human being. He eventually published his work, Treatise on Man, to much acclaim. Any deviation from Quetelet’s ideal human was considered abnormal. You undoubtedly know about the body mass index (BMI) because of gym class or your doctor. Those who fit the BMI ideal are praised and those who do not are made to feel less than. That was Quetelet’s work.
We experience the influence of Quetelet’s beliefs in other everyday objects, including classroom desks that favor right-handed people or features of a mobile app that are placed where the average user is likely to access it. You’ve likely heard of the 80/20 rule, which originally was used to describe how 80% of the land in Italy was owned by 20% of the population. The rule then became related to quality control: 80% of the problems come from 20% of causes. Designers eventually conflated the 80/20 rule with the bell curve, where the 80% represents the majority of users (and problems worth solving) and the 20% represents the edge cases (and problems not as worthy to solve).
The idea of an “average” human being is a myth. Here’s a story that puts that into perspective: In Quetelet style, the United States Air Force measured thousands of pilots and designed the cockpit of fighter jets to fit the average-sized man. When the program experienced a high percentage of crashes, a researcher went back and measured 4,000 pilots across 10 dimensions and found that none of the pilots fit the average.
This discovery eventually led to the design principles behind individual fit. It’s why we now have, for example, adjustable seats and other features in a car. Many designs intended for the mythical average end up serving no one. There is no such thing as average.
Data Is Just Data
Learning about Quetelet gives us perspective about how data can be misconstrued and used to embed enduring practices of exclusion. Data can expand our understanding of a situation or a problem, but it doesn't guarantee that we will create a better solution for people. Nowadays, especially, with our every device constantly tracking our behaviors and patterns, there are massive amounts of data available. This so-called big data is just data until we figure out how to make sense of it all.
We can add dimension to big data, however, by gathering information that helps us understand how people feel, think, and react. We want to understand the context of their behaviors and their motivations. This is what’s known as thick data and it complements big data. Where big data might show trends, thick data helps us dig deeper to understand the why. In terms of inclusive design, the combination of big data and thick data can help us identify areas of exclusion in products as well as in the context or environment.
If it’s unrealistic to design products that address every possible aspect of human diversity and there’s no such thing as normal, then how do we start? How do we figure out what questions or problems are worth solving for any one person or a group of people? This is where the persona spectrum comes into play.
In Unit 1, we described inclusive and universal design in this way: Universal design is one-size-fits-all. Inclusive design is one-size-fits-one. The persona spectrum is what we use in inclusive design to solve for one person and then extend to many. Let’s remember, this isn’t about the lowest common denominator. We are solving for mismatches in how people interact with products and doing so by learning from the people who experience the most mismatches.
What’s a persona spectrum? It’s a continuum of types of people who might interact with the product we’re designing. The spectrum focuses on human dimensions across the physical, cognitive, emotional, or societal aspects. We want to understand the range of dimensions, what mismatches there are, and why people want to interact with a product or solution.