Ten Ways Cognitive Biases Impact Data Design Work
Ten Ways Cognitive Biases Impact Data Design Work

These known biases are especially relevant in data visualization, and staying alert to them can help us avoid harmful designs

Our lives are plagued by uncertainty. We rely on design to navigate the twists and turns. When designs incorporate data, they can illuminate knowledge we may need.

Chart from CDC.gov

Designs incorporating data are best used when the context necessitates some important decision-making and when there is data available for guidance. For example, right now, the world is awash in charts meant to explain the pandemic, in part so we can better understand the risks and make decisions accordingly.

Whether they are aware of it or not, the designer often approaches such problems with a set of heuristics. These are broad rules, general principles, or mental shortcuts that help us make some quick decisions, such as using consistent language and making typefaces legible.

Heuristics are meant to help with problem solving but they can also present a new set of problems entirely. In the 1970s and 80s psychologists Amos Tversky and Daniel Kahneman laid the groundwork for studies of heuristics as well as cognitive biases.

Like heuristics, cognitive biases can be incredibly practical, but they can also lead to significant errors in perception, interpretation, judgment, and behavior. Stereotypes, for example, are cognitive biases.

While they are understandable as products of human evolution, cognitive biases have social impact that should not be underestimated. Racial profiling has contributed greatly to mass incarceration, and political polarization is at least in part the result of confirmation bias.

Designers can play a pivotal role in the presentation of information as well as its interpretation and impact. When we work with data, we have the opportunity to guide readers with facts, but there’s no guarantee that biases won’t play their part as well — our biases as well as the readers’. In fact, it’s pretty much guaranteed that they will.

Kahneman refers to heuristics and biases as “System 1” thinking, which is unconscious, easy, and dominant. “System 2” is slower, more deliberate and requires more effort and attention. System 2 thinking is the thinking needed to create data designs that allow readers to make decisions without jumping to harmful conclusions.

As designers, we must first bring our attention to our own biases before we can approach our work in this more intentional way. There are about 175 known cognitive biases to date. I will share some of the ones that I think are the most significant for designers to begin to understand as well as the problems they may present. When we slow down and think about the relationships between these biases and data storytelling, we can also consider how we might approach our work a bit differently.

1. We are in fact the biased ones.

Photo by Taras Chernus on Unsplash

At the start, we should recognize that there is a bias that may make us think only others are biased, and that we are less susceptible to bias (this is known as the bias blind spot). The irony is that this thinking actually makes us more susceptible to bias and more likely to dismiss any training or advice that could help us.

Biases operate unconsciously, so it doesn’t matter what we think consciously about ourselves (though the introspection illusion may tell us otherwise). It is important that we not learn about biases to simply point out errors in others, nor to rationalize our decisions. It is important that we be willing to spot errors in our own thinking so we can become better decision-makers. You can take some free online tests provided by Harvard to learn more about your own biases here.

2. We, too, resist information that conflicts with our beliefs.

We all tend to seek and find information that confirms and preserves our existing beliefs, and we tend to discount information that could challenge them (confirmation bias). When we are presented with new information, we might insist on sticking to our beliefs instead of revising them (conservatism bias).

How might this impact the way we collect, analyze, clean, and curate data sets to display? We might be inclined to select and use only data that tells the story we are personally most convinced of. We are likely reinforcing specific narratives that reflect the communities we are a part of.

3. We want to belong, but we may need to go against the grain.

Confirmation bias is made more challenging when we consider that we want to fit in, and thus we generally surround ourselves with people who think like us. This causes us not only to overestimate how much other people agree with our own beliefs but also to overvalue our own opinions (false-consensus effect). We also treat people we view as similar to us more favorably and we may treat others with intolerance or prejudice (in-group bias).

Further, the more people that believe a certain thing, the more likely we are to adopt that belief ourselves (bandwagon effect). We may say or do things based on whether they will be liked by others (social desirability bias). We may even blindly follow others rather than do our own independent thinking (herd mentality).

Photo by Antenna on Unsplash

These biases also point to some opportunities for growth. We can endeavor to connect with those different than us. As designers, this may mean we look to include those from different perspectives, disciplines, organizations, political parties, races, classes, gender identities, etc. It’s not enough to just know of people or meet them. If these relationships are going to help us check our biases, we must invest in going a step further and develop trust and honest communication in these relationships as well.

We can also experiment with intentionally inviting and incorporating different perspectives into our decision-making process. We can practice being a “devil’s advocate” and sharing outside-the-invisible-box ideas with others. We can ask ourselves and others “What ‘rules’ might we consciously break here and how might that affect the readers’ understanding of this data?”

4. Stories are important, and we need to consider their outcomes.

Stories are how we make sense of the world, so the way information is presented to us may determine our decision more than the information itself (narrative fallacy). We may also be more inclined to make decisions that make for better stories, despite the outcomes (framing bias).

These biases emphasize the importance of slow thinking for data design. Data always needs context, but we need to be mindful that the stories that we tell with data are a means to an end and we need to ask ourselves who might most benefit and who could be harmed by those ends. There are some good exercises for doing this thinking in this workbook from Creative Reaction Lab.

5. Stories are best when they balance both statistical trends and the experiences of real people.

We usually attribute our own actions to external influences, i.e. “I was late to work because of traffic.” But we are more likely to attribute others’ behaviors to their personality, i.e. “They are always late, why don’t they just give themselves more time for traffic?” (fundamental attribution error and actor observer and correspondence biases).

We can be moved by stories about others, though, even more moved than we might be by statistics about a large number of people (identifiable victim bias). In fact, we tend to think the probability of something happening is based on how many examples readily come to mind, which often leads to poor estimates and bad decisions (availability heuristic).

When we use both quantitative and qualitative data as part of our designs, we invite readers to consider both statistical data about external factors as well as how this data is reflected in personal experiences. We might also consider interactive designs that invite readers to find themselves in the data

6. Stories about people’s experiences should speak to both risk and opportunity.

Like anything else, we don’t like information that we think is “negative,” so we may avoid it (ostrich effect). Furthermore, we grossly underestimate the harm of such omissions (omission bias). We also tend to be too optimistic, overestimating the likelihood that good things will happen to us while underestimating the likelihood that negative things will, which leads us to take risks (optimism bias). This bias can be very difficult to reduce, but it can serve us if it gives us a sense of hope and motivation needed to pursue goals.

It may be best to balance qualitative data that recognizes the very real risks at play with qualitative data that highlights opportunities for positive change. This offers readers a more complete story and it supports them in making decisions based not only on external motivating factors but also on internal ones which are far more powerful.

7. Data can be a powerful persuader so we need to curate carefully.

We all tend to think of prior data as a reference point, which can skew our decision-making (anchoring bias). As designers, we have to decide if it is important to show prior data at all, and if so, how much to show. For example, if we were to show how much practices of slavery worldwide have decreased over the past 1,000 years, we may make it easier to dismiss the tragedy that it remains a practice today.

We also tend to be motivated by fear of loss far more than we are by desire for gain (loss aversion). The more loss one experiences, the more averse they are likely to become. As designers, we have to decide what to emphasize. If the issue is important, we can use this bias to our advantage and create a data story about some kind of loss or decline. However, especially if our readers are very sensitive to loss, we should be careful how much we lean on such a story as we may be supporting readers in overlooking positive potentials which might serve them better.

8. We love information and innovation, and that can make us biased.

These may be the biases that affect our profession the most. We more than others may be prone to seek more information even when it does not affect or encourage action (information bias). (Note, there is a different information bias discussed in epidemiology, but what I am referencing here is the cognitive bias discussed in psychology.)

When it comes to helping people make decisions, sometimes less is more. Eliminating chartjunk and maximizing data ink is only half the battle. We can also ask ourselves “Is this information even necessary?” I learned a great heuristic from John Maeda‘s book The Laws of Simplicity: “Subtract the obvious, add the meaningful.”

We may also be even more likely to be swindled by new ideas and technologies, overlooking limitations or weaknesses (pro-innovation bias). The basic principles of human-centered design help us remember that technological feasibility is just a fraction of good design, there must also be a real human need, identified through qualitative research. As designers, we must always take into account our ethical obligations and consider potential unintended consequences (remember omission bias). Lest we forget, many “cutting-edge” ideas have had tragic impacts on labor, displacement, environment, health, privacy, security, etc.

9. We can show them a chart hundreds of times, but we cannot make them change.

We are more likely to accept or favor an idea if we are exposed to it frequently (mere exposure effect). Relentless advertisers capitalize on this bias, and data designers may consider doing so as well, if the issue is important enough to warrant it.

We shouldn’t be too convinced of our ability to persuade, however. It is very easy to overestimate ourselves, or believe we have more influence than we really do (overconfidence bias and illusion of control). We definitely have a part to play, but we cannot be the only actors. It is best when we work with others who bring other talents to the table and can collaborate to to support a common goal.

10. It’s easy to focus on the products of our data design process, but we’d be wise to look at the process itself.

Looking back at events, we may focus on the outcome, believing it was inevitable (hindsight bias). It may be easy to overlook the process, which makes it hard to evaluate the decisions that were made (outcome bias). This is made worse by our memories, which are heavily influenced by things that happened after actual events (misinformation effect).

As designers, this can also affect how we analyze our own work and the work of others. It would be wise to shift our field away from showcasing our end products and toward documentation of and deep dialogue about our deliberate decision-making processes. Learning how to recognize and work with our cognitive biases is a good start.
['Lydia Hooper']
['Bias', 'Psychology', 'Data Science', 'Data Visualization', 'Storytelling']