The Principle of the Five Why’s and How Can You Use It Better Listen to Others

Photo Credit: Trung Nhan Tran

The Five Why’s is a common technique among UX researchers and other qualitative researchers that has personally transformed my approach to conversations. UX researchers interview people all the time, and to understand what they think about something, they always make sure to ask five “why” questions about their opinion in order to get to the heart of their opinion on the matter. Humans often rush into assumptions and judgements about what the other person thinks, and this forces us to slow down and get to the heart of how they view the world. 

Let’s consider a classic UX research example. Say you just developed a great new app, and you wanted to see whether people actually find it useful. So, you observe several people using the app and ask them what they think. The first person says, “I find it frustrating.” This is really useful information, but obviously, more details would help even more. So, a natural response would be, “Why do you find it frustrating?” 

Say the person gives a quick answer like, “I find the interface confusing, so I can’t do what I want to do” or whatever their frustration might be. This gives you a better understanding of their frustrations, but you can dig even more. According to the Principle of the Five Why’s you should ask at least five follow-up questions about why (or in some cases, how) they feel the way they do. 

This allows you to hone in exactly what their underlying needs and expectations are and how well your product meets those needs for them. Now, technically, not all follow-up questions have to be “why”. The idea is that like, “why” questions, ask questions that nonjudgmentally help uncover the underlying reasons for the opinions. For example, in this scenario, I may next ask, “What about the interface do you find confusing?” or “What are you trying to do, and how is it preventing you from doing it?” Both of these are not “why” questions, but they help orient me to understand why the person feels frustrated. Sometimes you have to learn some basic data about what their experience was before you uncover the next level of detail about why they had that experience. 

I often use this principle in regular conversations as well. Too often people assume they know what the person is thinking and make assessments based on their initial judgements. Asking follow-up questions forces us to slow down and consider in-depth what that person is trying to communicate. After listening, one can still disagree with a person’s conclusions, but at least you will know why. In almost every situation, I have found at least some points of agreement even when I thought we had opposing, conflictual perspectives. 

It also calms you down. In tense conversations, we often simply react. Maybe we presume they meant something hostile and respond in turn. This helps us survive threats but clouds our ability to empathize with others and reason through their ideas. Asking questions allows us to pause and reflect for a few more moments on what else might be influencing where they are coming from. 

Feel free to try it in regular conversations, especially potential arguments or other tense conversations. Pause and ask a few “why” questions to understand the layers behind their thoughts before launching into your perspective on the matter. It will change the course of the conversation. Worst case scenario, by the end of it, you will still disagree with them just as much as you did initially, but often you will learn something and will discover a way to carry on nonconfrontationally in a way that involves both of you getting what you want. If you disagree, you have lost little by hearing them out and gained the ability to disagree productively since you now know exactly where the other person is coming from. 

Now in every interaction, you don’t have to literally ask five questions. That exact number may not fit every interaction. The spirit of the rule is to ask follow-up questions that force you to engage with the reasons underneath someone’s impressions. For me, I often ask follow-up questions until it feels uncomfortable, until I feel my thoughts well up so strongly within me that I am eager to jump in. Then, I ask just two more follow-up questions. In the unlikely event that I still think they are totally wrong by the end of those two questions, I can jump in with my perspective. This slows me down and forces me to practice more constraint and helps me see a path to empathize and/or disagree in a positive and productive manner.

Data-Driven Diversity: How Evidence-Based HR Can Create Equitable Organizations (with Élide Souza)

What is it like to use data science to understand employees in an organization to help improve people’s experiences at the firm? In this next podcast interview, I spoke with Élide Souza, a people’s data science at the Brazilian bank, Banco BV. She manages a data science team that researches how to improve employee’s experience and increase diversity.

This is part of a new trend called “People Analytics” where organizations hire data scientists within their HR (Human Resources) departments to conduct social science research in order to help improve organizational culture. In our conversation, she describes how she approaches such social research, including how she addresses potential bias, approaches intervention, and navigates the ethical implications of such work.

As a fellow social science-focused data scientist, I find this work fascinating.

Conversing with AI: Interview with Chelsea Wang about Communications with Artificial Intelligence Systems (Part 3 of 3)

In the final part of our conversation, Chelsea Wang explains how her background in psychology has influenced her work in artificial intelligence. In particular, she describes how her social science background helped her develop and deploy her own version of the Mutual Theory of Mind as a psychologist within the field of artificial intelligence. When socializing, humans employ a recursive feedback loop of conceptualization of each other, and she explores the application of similar concepts to conversational AI systems.

She concludes by discussing her journey as a PhD student: what led her to seek her dissertation and her plans afterwards to use what she is learning now to conduct innovative and impactful work in the business world.

Click here to learn more about the Interview Series.

More about Chelsea:

Qiaosi Wang (Chelsea) is a fifth-year PhD candidate in Human-Centered Computing at Georgia Institute of Technology. Chelsea is a human-centered AI researcher and her PhD dissertation work focuses on building the Mutual Theory of Mind framework, inspired by the basic human capability to surmise what is happening in others’ minds (also known as “Theory of Mind”), to enhance mutual understanding between humans and AIs during human-AI communication. Her work specifically focuses on the human-AI communication process during AI-mediated social interaction in online learning, where AI agents can connect socially isolated online learners by providing personalized social recommendations to online learners based on information extracted from students’ posts on the online class discussion forums.

Chelsea received her Bachelor of Science degrees in Psychology and Informatics from the University of Washington, Seattle. In her free time, Chelsea loves hiking, playing with her cat, Gouda, and spending time at bouldering gyms. 

To learn more about Chelsea and the sources we referenced in our conversation:

Conversing with AI: Interview with Chelsea Wang about Communications with Artificial Intelligence Systems (Part 2 of 3)

Chelsea Wang has spent many years trying to improve the cognitive process of artificial intelligence systems to better interact with humans. In this second part of our conversation, she explains her theories about metacognition, intelligence, and potential anthropomorphization of AI “thought” processes. Through this, she explicates her vision and approach to the potential social life of AI.

Click here to learn more about the Interview Series.

More about Chelsea:

Qiaosi Wang (Chelsea) is a fifth-year PhD candidate in Human-Centered Computing at Georgia Institute of Technology. Chelsea is a human-centered AI researcher and her PhD dissertation work focuses on building the Mutual Theory of Mind framework, inspired by the basic human capability to surmise what is happening in others’ minds (also known as “Theory of Mind”), to enhance mutual understanding between humans and AIs during human-AI communication. Her work specifically focuses on the human-AI communication process during AI-mediated social interaction in online learning, where AI agents can connect socially isolated online learners by providing personalized social recommendations to online learners based on information extracted from students’ posts on the online class discussion forums.

Chelsea received her Bachelor of Science degrees in Psychology and Informatics from the University of Washington, Seattle. In her free time, Chelsea loves hiking, playing with her cat, Gouda, and spending time at bouldering gyms. 

To learn more about Chelsea and the sources we referenced in our conversation:

Conversing with AI: Interview with Chelsea Wang about Communications with Artificial Intelligence Systems (Part 1 of 3)

Chelsea Wang describes her work developing and refining the communication processes between artificial intelligence and humans, particularly the Mutual Theory of Mind framework she has helped build. As a doctoral student in Human-Computer Interaction, she also discusses her journey from human psychology to the social interactions of AI.

Click here to learn more about the Interview Series.

More about Chelsea:

Qiaosi Wang (Chelsea) is a fifth-year PhD candidate in Human-Centered Computing at Georgia Institute of Technology. Chelsea is a human-centered AI researcher and her PhD dissertation work focuses on building the Mutual Theory of Mind framework, inspired by the basic human capability to surmise what is happening in others’ minds (also known as “Theory of Mind”), to enhance mutual understanding between humans and AIs during human-AI communication. Her work specifically focuses on the human-AI communication process during AI-mediated social interaction in online learning, where AI agents can connect socially isolated online learners by providing personalized social recommendations to online learners based on information extracted from students’ posts on the online class discussion forums.

Chelsea received her Bachelor of Science degrees in Psychology and Informatics from the University of Washington, Seattle. In her free time, Chelsea loves hiking, playing with her cat, Gouda, and spending time at bouldering gyms. 

To learn more about Chelsea and the sources we referenced in our conversation: