Biases in UX: A Guide to Awareness and Mitigation

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In the ever-evolving field of User Experience (UX) design, one critical element often overlooked is the impact of bias. Bias, in its various forms, can significantly distort the research process and, consequently, the final design. As UX designers, it's essential to recognise these biases to create more inclusive and effective products. In this blog, we'll explore what biases are, list the most common biases encountered in UX design, and provide examples to help you understand and mitigate their impact.

What is a Bias?

Bias refers to a prejudice or favouritism towards something based on limited or skewed information. In the context of UX design, biases can lead to skewed research findings, ultimately affecting the usability and inclusiveness of the product. It's important to note that everyone has biases, many of which are unconscious, but being aware of them can help us take steps to minimise their effects.

Types of Biases

Psychology has identified over 180 cognitive biases that influence how we see the world and make decisions. These biases are hardwired into our thinking, subtly shaping our judgments and actions in different situations. In UX design, being aware of these biases is essential for creating user-focused products. While many biases come into play, here are six that often affect UX design: Confirmation Bias, False Consensus Bias, Primacy and Recency Bias, Implicit Bias, the Sunk Cost Fallacy and the Anchoring Effect.

Common Biases in UX Design

1. Confirmation Bias

Definition: Confirmation bias occurs when a researcher or designer looks for evidence to support a preconceived notion or hypothesis, ignoring evidence that contradicts it.

Confirmation Bias. Example in UX: Suppose you believe that younger users prefer minimalist design. During user research, you might selectively focus on feedback from younger participants that supports this belief while overlooking contradictory feedback from older users or those who prefer more detailed interfaces.

Confirmation Bias. Example outside of UX:

  • Only paying attention to information that confirms your beliefs about big issues such as politics or and global warming
  • Only following people on social media who share your viewpoints
  • Choosing news sources that present stories that support your views
  • Refusing to listen to the opposing side
  • Not considering all of the facts in a logical and rational manner

Mitigation: To avoid confirmation bias in UX research, ask open-ended questions during interviews, listen carefully without pushing your own opinions, and make sure to include a diverse group of users in your research.

2. False Consensus Bias

Definition: False consensus bias is the tendency to assume that others share the same beliefs or opinions as you do, leading to a distorted view of how widely a particular opinion is held.

False Consensus Bias. Example in UX: If you believe that everyone enjoys dark mode because you do, you might overestimate the popularity of this preference and design with it as a default, neglecting users who prefer or need a lighter interface.

False Consensus Bias. Example outside of UX:

  • Assuming that everyone in your community shares your political beliefs because most people you interact with do
  • Being surprised or confused when encountering someone with opposing views
  • Believing that your favourite TV show is universally loved because your friends and family all enjoy it
  • Thinking that your dietary choices (e.g., vegetarianism) are the norm because those in your social circle follow similar diets
  • Underestimating the diversity of opinions on a controversial topic because you predominantly engage with people who agree with you

Mitigation: Regularly question your assumptions by clearly stating them and testing them with a wide, diverse group of users. This helps ensure your design isn't overly influenced by your personal preferences.

3. Primacy and Recency Bias

Definition: Primacy bias refers to the tendency to remember the first piece of information we encounter, while recency bias is the tendency to recall the most recent information more vividly.

Recency Bias. Example in UX: After conducting several interviews, you might find yourself overly influenced by the first or last participant’s feedback, potentially neglecting important insights from other participants.

Primacy Bias. Example outside of UX:

  • Remembering the first candidate you interview for a job more clearly than subsequent candidates, leading you to favour them
  • Being more influenced by the first argument in a debate, even if later arguments are more compelling
  • Making a strong impression of someone based on their first actions or words, and letting that initial impression dominate your opinion of them (first impressions count!)
  • Focusing on the first piece of feedback you receive in a performance review and allowing it to colour your perception of your overall performance
  • Judging a book by its cover or first chapter, regardless of how the rest of the book unfolds

Recency Bias. Example outside of UX:

  • Favouring the last candidate in a series of interviews because they are freshest in your memory
  • Recalling the last items on a shopping list more easily than those in the middle
  • Being influenced more by the most recent news story you heard about a politician when forming your opinion, rather than considering their entire record
  • Evaluating a film based primarily on how it ended, rather than the whole experience
  • Remembering the last speaker at a conference more vividly and giving more weight to their ideas, even if earlier speakers had equally valid points

Mitigation: Take detailed notes or record interviews to ensure that you can accurately recall all the information gathered, not just what stood out most. Reviewing these notes helps balance out any primacy or recency effects.

4. Implicit Bias

Definition: Implicit bias involves the unconscious attitudes or stereotypes that affect our understanding, actions, and decisions. This can manifest in UX design when certain user groups are either overrepresented or underrepresented in research due to unconscious assumptions.

Implicit Bias. Example in UX: If a design team predominantly interviews young, tech-savvy users, their implicit bias might lead them to neglect the needs of older users or those with different levels of technological proficiency.

Implicit Bias. Example outside of UX:

  • Automatically assuming that a person’s job or role is based on their gender (e.g., assuming a nurse is female or an engineer is male)
  • Feeling more comfortable around people who share your ethnicity, and unconsciously avoiding those who don’t
  • Assuming someone’s level of education or intelligence based on their accent or way of speaking
  • Making assumptions about someone’s socioeconomic status based on their clothing or appearance
  • Unintentionally favouring candidates from similar educational backgrounds or social circles during the hiring process, while overlooking equally qualified candidates from different backgrounds

Mitigation: Strive to include a wide range of participants that reflect the diversity of your target audience. Reflect on your own behaviours and invite feedback from others to identify and address any biases in your research process.

5. Sunk Cost Fallacy

Definition: The sunk cost fallacy occurs when a designer or team continues to invest in a particular design or feature simply because they’ve already invested considerable time and resources into it, even when it’s clear that it’s not meeting user needs.

Sunk Cost Fallacy. Example in UX: You’ve spent weeks developing a new navigation system for your app, but testing reveals that users find it confusing. Despite this, you continue refining the flawed design instead of exploring alternative solutions.

Sunk Cost Fallacy. Example outside of UX:

  • Continuing to watch a bad film just because you've already spent an hour watching it
  • Staying in an unfulfilling relationship because of the time and emotional energy you've already invested in it
  • Persisting with a failing business venture because you've already invested significant money and resources, even when it's clear it won’t succeed
  • Continuing a university course you no longer enjoy or find useful because you’ve already completed several terms.
  • Refusing to change a long-term strategy at work, despite evidence it’s not effective, because the company has already spent a lot of time and money on it

Mitigation: Break down projects into smaller phases with clear decision points. At each stage, evaluate whether to continue, pivot, or abandon the current approach based on user feedback and new insights.

6. Anchoring Effect

Definition: The anchoring effect occurs when an initial piece of information, such as an estimate or figure, heavily influences subsequent decisions, even when later evidence suggests that a significant change is necessary. This bias can lead to inflexible thinking and decisions that are overly tied to the initial anchor, rather than being responsive to new information.

Anchoring Effect. Example in UX: A client asks you to provide a ballpark price for completing a project, and you give them an estimate based on the initial scope. As the project progresses, the scope expands significantly, doubling the amount of work required. However, despite the increased workload, the client continues to expect the project to be completed for the original price. The initial estimate becomes an anchor for the client, making it difficult for them to accept the need for a higher cost that better reflects the true scale of the work involved. This highlights how the anchoring effect can lead to unrealistic expectations, making it challenging to adjust pricing as projects evolve.

Anchoring Effect. Example outside of UX:

  • Real Estate Pricing: A seller sets an initial asking price for a house, and potential buyers use that price as an anchor. Even if the house has issues or the market conditions change, buyers may struggle to negotiate significantly lower prices, feeling that the initial price is a reference point.
  • Salary Negotiation: During a job interview, a candidate mentions their current salary, and the potential employer uses this as an anchor when making an offer. Even if the role warrants a higher salary, the employer may offer a figure close to the candidate's current pay, influenced by that initial number.
  • Retail Discounts: A shop lists an item at an inflated original price and then marks it down, making the sale price seem like a great deal. Shoppers, anchored to the original price, perceive the discount as more significant than it might be in reality.
  • Budgeting for Events: When planning an event, an initial budget is set based on early estimates. As the event grows in size or complexity, it becomes difficult for the planners to adjust the budget adequately, with the initial figure anchoring their expectations.
  • Starting Bid in Auctions: In an auction, the opening bid often serves as an anchor, influencing the subsequent bidding process. Bidders may base their decisions on the initial bid rather than the true value of the item, leading to higher final prices.

Mitigation: To overcome the anchoring effect, it’s essential to establish from the outset that initial estimates are provisional and subject to change as the project evolves. Regularly review and update your estimates in light of new information, and communicate these changes clearly to your clients. This approach ensures that decisions are based on the current scope of work, not constrained by an initial figure that no longer reflects the reality of the project.

Recognising and Overcoming Biases

Biases in UX design are inevitable, but by recognising them, we can actively work to minimise their impact. Regularly challenging assumptions, diversifying research samples, and staying open-minded are key practices to embrace in order to create fair, inclusive, and effective user experiences.

By understanding and addressing these biases, we not only strengthen the integrity of our research but also ensure that the end products truly meet the needs of all users, not just a select few.

Bias awareness is an ongoing process. The more we practise identifying and countering biases, the better we become at designing with empathy and inclusivity at the forefront. It's important to consistently apply these insights to build products that are both functional and equitable for everyone who interacts with them.