Flag

We stand with Ukraine and our team members from Ukraine. Here are ways you can help

Get exclusive access to thought-provoking articles, bonus podcast content, and cutting-edge whitepapers. Become a member of the UX Magazine community today!

Home ›› Artificial Intelligence ›› How Can User Experience Research (UXR) Help Build Users Trust in AI Systems and Increase Engagement?

How Can User Experience Research (UXR) Help Build Users Trust in AI Systems and Increase Engagement?

by Celine Lenoble
4 min read
Share this post on
Tweet
Share
Post
Share
Email
Print

Save

With ML facing so many users, there is a case to approach the conception and design of ML-powered applications from a UX research perspective. Read more to find out.

Today, Artificial Intelligence (AI) and, more specifically, Machine Learning are pervasive in our daily lives. From Facebook ads to YouTube recommendations; from Siri to Google Assistant; and from automated translation of device notice to marketing personalization tools; AI now deeply permeates both our work and personal lives.

This article is the first in a series of three that advocate for renewed UX research efforts in ML apps.

With ML facing so many users, there is a case to approach the conception and design of ML-powered applications from a UX research perspective.

This lies on three main reasons:

  1. Mental models of users haven’t caught up with how ML and AI truly work.
  • UXR can uncover existing mental mentals and help design new ones that are more suited to this new tech.

2. ML and AI can have an insidious and deep impact on all users’ lives

  • UXR reveals the myriad of intended and unintended effects of apps on people’s life — and help build more ethical AI.

3. ML and AI can have disparate impacts on individuals based on their ethnicity, religion, gender, sexual orientation:

  • UXR can also help address some of the sources of bias.

In this episode, we will focus on the first reason: How can UXR help build trust in AI systems and increase users’ engagement?

ML and Real Users

Users’ attitudes towards ML-powered apps are complex. Algorithm aversion has been well studied and documented:

In a wide variety of forecasting domains, experts and laypeople remain resistant to using algorithms, often opting to use forecasts made by an inferior human rather than forecasts made by a superior algorithm. Indeed, research shows that people often prefer humans’ forecasts to algorithms’ forecasts (Diab, Pui, Yankelevich, & Highhouse, 2011; Eastwood, Snook, & Luther, 2012), more strongly weigh human input than algorithmic input (Önkal, Goodwin, Thomson, Gönül, & Pollock, 2009; Promberger & Baron, 2006), and more harshly judge professionals who seek out advice from an algorithm rather than from a human (Shaffer, Probst, Merkle, Arkes, & Medow, 2013).

Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114–126. 

However, their research shows that this algorithm aversion phenomenon appears only once humans witness, or are made aware of, forecasting errors. In 2019, Logg J.M., Minson J.A., Moore D. A. demonstrated the contrary, that humans show an initial appreciation towards algorithm advice compared to fellow humans:

Our participants relied more on identical advice when they thought it came from an algorithm than from other people. They displayed this algorithm appreciation when making visual estimates and when predicting: geopolitical and business events, the popularity of songs, and romantic attraction. Additionally, they chose algorithmic judgment over human judgment when given the choice. They even showed a willingness to choose algorithmic advice over their own judgment.

Logg J.M., Minson J.A., Moore D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, vol. 151, March 2019, 90–103.

ML and The Theory of Machine

One possible explanation still being investigated is “The Theory of Machine” (equivalent to the “Theory of Mind” for humans) that people operate with. The Theory of Machine, or more simply, mental models, as designers call it, is a series of assumptions humans make on how an application works internally.

One such assumption is the idea of a fixed mindset. Having a fixed mindset in psychology means you believe people have a certain amount of intelligence or skills, and they can’t do anything to increase that amount. Applied to a Theory of Machine, it means that people believe that a computer program output is fully determined by the initial input and not capable of learning or evolving.

The fixed mindset applied towards traditional software was appropriate for a long time. Your typical software, word processor, or spreadsheet was not capable to improve on its own and learn from its mistakes. The user might expect changes following an update, but otherwise, they expect the program to behave consistently over time.

When confronted with ML-powered applications, users continue to apply the classic fixed mindset mental model. So, once they experience what they perceive as the app making a mistake, they completely lose trust in the system’s ability to give accurate results. This is possibly what triggers the shift to algorithm aversion, after an initial appreciation.

Numerous ML apps presents themselves as an assistant. They draw on the mental model of a relationship with a person, hoping to change the assumptions users make on how the program works.

This choice of mental model presents several challenges:

  • AI is not (yet) powerful enough to pass for a human: Users’ expectations are shaped by how they expect a human to respond, and users typically end up extremely disappointed, if not infuriated, by the AI behavior.
  • Even for their fellow humans, people tend to apply a fixed mindset and rarely allow for the possibility of growth and change in capabilities, at least not in any short time frame.
  • If users do have a growth mindset in relation to humans, meaning that they believe humans can improve provided they are given the opportunities to learn or they are taught what to do, this mindset doesn’t transfer well to AI assistants, because the learning modalities of humans and AI are so different.

Mental Model and User Engagement with ML Apps

What mental model should you use then? There is no one-size-fits-all answer to this question. This is where User Experience Research is required:

  • to uncover the existing mental models associated with specific tasks,
  • to experiment with multiple UI metaphors beyond the assistant, and
  • to help users adjust their existing mental models and expectations to the reality of ML-powered apps.
post authorCeline Lenoble

Celine Lenoble

I am Director of UX Research at brainlabs where I lead a team of CRO analysts and UX researchers with diverse backgrounds. I believe in a holistic approach to UX research and design combining all perspectives: Human Computer Interaction, design thinking, psychology, sociology, anthropology and all methods: from big data to ethnographic study. I am particularly interested in the UX of ML-powered products & services. Disclaimer: opinions represented here are personal and do not represent those of brainlabs.

Tweet
Share
Post
Share
Email
Print
Ideas In Brief
  • The article covers the conception and design of ML-powered applications from a UX research perspective.
  • The author unpacks the following ideas:
    • Machine Learning and Real Users
    • Machine Learning and The Theory of Machine
    • Mental Models and User Engagement with Machine Learning Apps

Related Articles

Discover how AI-powered gesture-based navigation is redefining app experiences, making interactions more intuitive and personalized. Explore the opportunities and challenges of this design revolution.

Article by Kevin Gates
Designing Serendipity
  • This article explores the role of AI in enhancing app navigation through gesture-based interactions, emphasizing a shift from traditional menus to intuitive, swipe-driven experiences.
  • It examines the intersection of AI and interaction design, highlighting how machine learning can support user discovery by anticipating needs and surfacing relevant content.
  • The piece critically assesses the potential of gesture-based navigation to improve accessibility, user engagement, and overall app usability, while addressing design challenges and potential pitfalls.
Share:Designing Serendipity
11 min read

Discover how AI is changing UX research. It’s not just making data analysis faster. It’s also encouraging people to think more deeply. Learn how to strike a balance between human insight and AI-driven efficiency to create more thoughtful designs.

Article by Charles Gedeon
How AI and Metacognition Are Shaping UX Research
  • The article talks about how AI can speed up data analysis and encourage people to think more deeply about biases and missed insights, which can improve the quality of user-centered design.
  • It shows that AI-powered UX research tools need to include reflection checkpoints. These checkpoints let researchers critically assess their assumptions and conclusions.
  • The piece highlights the collaboration between AI’s ability to recognize patterns and human judgment to make sure the research outcomes are meaningful and consider the context.
Share:How AI and Metacognition Are Shaping UX Research
4 min read

How can thoughtful workspace design transform collaboration and creativity? Discover how a human-centered approach reimagined 21,940 square feet into a flexible, inspiring environment that employees love.

Article by Aalap Doshi
Rethink Space: Designing a Human-Centered Workspace that Supports Flexibility, Collaboration, Privacy, Innovation, Creativity, and Transparency
  • The article explores how human-centered workspace design can improve collaboration, flexibility, and creativity by addressing employee needs.
  • It highlights solutions like open zones, quiet spaces, and pod-like configurations, showing how these changes boosted teamwork and morale.
  • The piece emphasizes the value of co-creation, adaptability, and clear communication in rethinking office spaces.
Share:Rethink Space: Designing a Human-Centered Workspace that Supports Flexibility, Collaboration, Privacy, Innovation, Creativity, and Transparency
7 min read

Join the UX Magazine community!

Stay informed with exclusive content on the intersection of UX, AI agents, and agentic automation—essential reading for future-focused professionals.

Hello!

You're officially a member of the UX Magazine Community.
We're excited to have you with us!

Thank you!

To begin viewing member content, please verify your email.

Tell us about you. Enroll in the course.

    This website uses cookies to ensure you get the best experience on our website. Check our privacy policy and