Building Users Trust in AI through UX/UI Design
Artificial intelligence (AI) has become a cornerstone in enhancing user experience (UX) design. The fusion of AI with UX design holds immense potential, offering unprecedented opportunities to tailor trusted user interface. However, with great power comes great responsibility. The key to unlocking the full potential of AI in UX/UI design lies in one crucial factor: trust.
To gain deeper insights into this dynamic field, we sat down with Anna Demianenko, our Lead of AI Innovation, who has been at the forefront of designing trusted user interfaces for AI products that users love. Her expertise in integrating cutting-edge AI trends with a keen focus on user needs, privacy, and ethical design principles offers valuable lessons for anyone looking to navigate the complexity between technology and human-centric design.
What is a trusted user interface?
A Trusted User Interface focuses on making digital systems secure and trustworthy. It does this by clearly communicating with users, ensuring only authorized users can access data, and keeping information safe. Users have control over their data and get helpful messages if something goes wrong. TUIs also protect against common online threats like phishing. They're important for making us feel safe when using apps, especially for things like banking or healthcare, where privacy is really important.
You also design AI interfaces, how AI and TUI user interfaces relate?
First I should answer whether AI can be a trusted user interface feature. It can. AI can enhance security by detecting and preventing threats in real-time, such as identifying suspicious login attempts or unusual user behavior patterns.
Moreover, an AI interface can also be considered a trusted UI. When AI capabilities are integrated into a user interface design that prioritizes security, transparency, and user trust, it becomes a TUI.
Anna, as we stand at the forefront of 2024, could you share your perspective on the most exciting trends and innovations currently shaping AI UX design?
So, the most recent trends are extreme. Let's start with user personalization: The more AI learns what users like, the more designers learn how to automate processes and interface features, and the more personalized interfaces become. I envision this as the most prominent trend of this year, but that is not the only one.
Other trends include data collection, training, and investing in data sets. This would be the second most important trend of artificial intelligence in general. Even though it's not directly related to interface design, it is the most important part of the artificial intelligence user experience because data sets underlie anything that users see on the front end. Basically, it shapes artificial intelligence decisions and how it acts. So yeah, investing, creating, and acquiring unbiased and ethically correct data sets are among the biggest trends in 2024. So that would be my answer to this question.
The third trend is the transparency of data ownership and company policies. This is also closely related to Gen Z becoming the new generation in charge. And those guys do hold everyone accountable. So, policy and data transparency will also become a trend in 2024.
In your experience, how critical is a user-centric approach in AI UI/UX design for building trust, and what strategies do you employ to ensure this?
So, here, it is important to understand that a user-centric approach is not only about building trust but also understanding what is going on in the heads of our users. So it's also about understanding what trust for them is and what brings them mistrust about artificial intelligence. And with every single industry, it can be something different.
So, to ensure trust using a user-centric approach, you first need to understand your users and their main concerns, needs, and pains in the niche in which you are trying to implement artificial intelligence. Right? So that would be step one.
Step two would be understanding what trust is for the users and what brings them trust and safety. It is also important to understand what scares them the most. Again, with our trends of 2024, transparency, personalization, and good UX, you can gradually grow trust in the AI products you're building. So that's it.
Considering your perspective on the role of empathy in AI from both the developers' and the users' viewpoints, how does this duality of empathy influence the design strategies employed in AI interfaces?
Discussing empathy goes both ways - Empathy from the user to artificial intelligence or from you as a developer of artificial intelligence to your creation.
As a machine learning algorithm or an AI-based product engineer, you need to empathize with AI using this simple formula and question. So, for instance, you have an idea, right? You have an idea of task automation. Ask yourself if the task can be explained to an intern. Can an intern in your company or any other company perform this task? If they can, your imaginary intern, I mean, then artificial intelligence will do that. If an intern cannot deal with that task, then the task is probably a bit complicated for artificial intelligence. This brings a lot of empathy towards artificial intelligence.
So, it lets us understand that AI is not something complex or scary, and it's just an algorithm that learns and repeats what we teach.
Artificial intelligence empathy towards the user comes from trial and error, feedback loops, and continuous learning, which can be expressed through the trusted user interface. So, what do we do with the AI interfaces? By learning from user behavior, we ensure empathy goes both ways.
We can set some triggers and observe. For instance, with chatbots or any text-digesting AI, we can learn from specific behavior in terms of the number of clicks, misclicks, and actions that the user has to redo. This means that the action was not done properly for the first time. So it has to be redone. It can be done by gathering feedback and observing how users interact with the hub. If combined with extreme user personalization, creating a user profile can be used to understand and empathize with the user.
Every innovation comes with its hurdles. What are the biggest challenges you face in integrating AI into digital products, and how do these impact user trust?
The biggest challenge is that right now, every single product strives to put AI in its name because this gains investments, but it does not necessarily sell to the end user and does not necessarily have a market feed.
So, what we are seeing now is an extreme oversaturation of the market with those AI products that have no market feed and cover no user needs, and this is going to be a challenge. Such practices will overheat the market with those AI products, and users will lose even more trust in real AI products that serve a purpose.
So, I see how we can overcome this challenge by approaching product design mindfully, doing proper research to understand our user's needs, and tailoring the product to cover them. Yeah, that's pretty much it.
Transparency is key to building a trusted user interface technology. How do you create AI interfaces ensuing transparency?
Okay, so there are very clear rules to ensure transparency. Before starting to design your trusted user interface, you should determine how to show model confidence, if you decide to show it at all. The idea here is that as a designer or developer, you understand what a system can and cannot do, and then you clearly communicate that with the interface. It can be done during the onboarding or when a user tries to perform some action. Either way, you must help users understand what the AI system can do.
Also, clarify how well the system can do what it can. So you can show how often the AI system may make mistakes or what time frame or data period the database was based on. Some data may be limited or outdated. For instance, you definitely may want to show snippets of which articles, resources, or other data sets were used in AI's reply output.
And, of course, you always need to continuously do this two-way process of gathering feedback. It is important not only to show some output but also to gather feedback from the users to evaluate how well those set expectations meet reality and how users are satisfied with set expectations.
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Ethics in AI is a hot topic. What ethical considerations do you prioritize in AI UI/UX design to maintain user trust?
Okay, so it all starts with datasets. So, this is, unfortunately, something that designers cannot control. But I do hope that we can somehow affect the product design process and inspire our developer colleagues to be more responsible about data. So, the idea is that the data has to be directly checked to ensure that it's clear, representative, and has no biases. And yeah, that should pretty much come from a reliable source.
The next step in checking the clearness of a dataset is to create a set of, let's say, company values or norms or your internal moral ethical compass. You need to evaluate whether your data complies with those ethics or the rules you created. Again, check for biases and inclusivity. And last but not least, of course, protect user data. Ensure that anything users put in the interface is secure - personal data, inquiries, their behavioral patterns, pretty much anything. The protection of user data goes separately from the unbiased and inclusive dataset on which the AI model is based. Still, it's something that needs to be taken into consideration. So yeah, directly on the interface, it's very hard to implement the protection of user data. But whenever we talk about the full product design process, this is where we have to set our priorities to plan or invest more time.
How do you incorporate feedback mechanisms in AI interfaces to enhance both trust and the user experience?
Okay, I've talked a little bit about the feedback loop before. Again, the ways to collect feedback are observing users' behavior, tracking sentiments, asking the users for feedback, and remembering recent interactions. The key is to encourage feedback somehow. Well, by encouraging, I mean you need to somehow make it engaging; you have to make it rewarding. Also, it helps to explain the value of giving feedback. For example, telling the users that the system learns and becomes better for usability through their feedback.
You can also explain how the consequences of this feedback and reactions impact the AI system's future behaviors. And, of course, notify users about changes. Closing this feedback loop is vital, which helps provide transparency. Right? So you gathered feedback somehow, and for instance, the data shows that users were unhappy with the chatbot's response, and they started angrily clicking all around the interface. Or they started asking the same question repeatedly because they were unsatisfied with the response. Your system needs to be able to recognize this behavior as something unwanted and correct it. So, it regenerates responses to notify the users such as, "Oh, I'm so sorry," "This was taken into consideration," "We changed this because you acted like this," "You provided this feedback," or "Based on your behavior, we analyzed and realized that this was not what you expected, so we corrected our output accordingly."
This feedback loop covers pretty much all the steps, all the trends, and all the principles that I talked about before. It goes from personalization to extreme user understanding, learning from user behavior, gathering feedback, and providing transparency on an explanation of how AI makes decisions or how AI acts or reacts. So this is like a full cycle of what we just talked about.
Balancing personalization with privacy concerns remains a hot topic. How do you envision the future of personalization in AI design, considering the evolving landscape of privacy regulations and user expectations?
Yeah, this is what I've been talking about. You should make the protection of user data one of your main priorities. So, suppose we're talking about priorities in the AI product design process. In that case, I think the majority of time should be invested into, as I said before, checking the database for AI, first and second into security, the protection of both the database and user data.
How can you do it on design? You can't. It's all about development and security. But you can communicate with your users. You can advocate for users to be the owners of their data and for them to choose which data to share. Give them freedom and more control. Right, the user control that we were talking about earlier. We can also make it transparent about how the data is stored or used or what happens with it. This is what we can do on the interface of the product.
Instead of conclusion
Throughout our conversation with Anna, it's clear that designing AI interfaces that users trust involves much more than just technical know-how; it involves security-sensitive human interactions and much more. It requires a deep understanding of human behavior, a commitment to ethical principles, and a relentless focus on user-centric design.
As we look towards the future, the insights shared by Anna highlight the importance of empathy, transparency, and user control in creating AI systems that are not only intelligent but also respectful of user needs and concerns. By embracing these principles, designers and developers can create AI interfaces that not only meet the technological demands of 2024 but also foster a deeper sense of trust and collaboration between humans and machines.
Well, you guys have heard from the expert and if you have any more questions, you can contact us directly at hello@lazarev.agency
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