AI Needs UX: Why Good Design is the Key to Ethical AI

Artificial intelligence (AI) is becoming a bigger part of UX design. Designers now work with AI both as a key feature of digital products and as a tool to improve UX processes. But despite AI’s potential, a lot of AI projects fail. Surprisingly, the reasons are not technological but related to UX design: poor use case selection, lack of testing, ignoring real-world constraints, and lack of transparency.

Although I haven’t worked on AI products directly, my research on the topic has shown me how crucial UX design is in AI development. In this article, I’ve gathered key principles that can help designers create user-friendly and ethical AI solutions.

UX Designers and AI: collaboration, not replacement

There's a common myth that AI will replace UX designers. In reality, AI is a tool that enhances a designer’s capabilities. The designer’s role doesn’t disappear—it evolves:

Defining user scenarios – UX designers identify where AI can truly add value, uncover pain points, and design optimal user experiences.
Creating clear and explainable interfaces – For users to trust AI, interfaces must be understandable, and AI algorithms should be predictable and transparent.
Ensuring ethics and trust – AI should not mislead users or hide its logic. UX designers build interfaces that promote transparency and trust.
Balancing automation and control – AI should assist users but not take away their control. Designers create interaction models that ensure a healthy balance between automation and user input.
Reducing bias – UX designers work with data scientists and developers to detect and eliminate bias in data and algorithms, ensuring inclusivity and fairness.

Key areas of UX Design in AI products

UX designers can contribute to AI in two major ways:

Designing UX for AI-powered products – Creating user-friendly interfaces where AI plays a central role, such as:

Voice assistants and chatbots
Recommendation systems (Netflix, Spotify)
AI-driven medical assistants
Automated financial platforms

Using AI to improve UX design – Leveraging AI tools to automate design processes, such as:

Analyzing user data to identify pain points
Automating repetitive UI design tasks
Predicting user behavior and personalizing interfaces

Common UX mistakes in AI projects

Many AI products fail due to UX-related issues:

Wrong use case selection – AI should solve real problems, not be used just because it’s trendy.
Lack of prototyping – AI UX design requires thorough testing and an iterative approach.
Ignoring real-world limitations – AI faces technical, legal, and ethical constraints that must be considered.
Training AI on unrealistic data – If AI is trained on biased or incomplete data, it will produce poor results.
Failing to address bias in data – AI can reinforce discrimination if trained on biased datasets.
Not focusing on AI learning and corrections – AI systems need continuous monitoring and adaptation to changing conditions.

Key UX principles for AI products

Explainability – AI decisions should be easy to understand, especially in critical areas like finance, healthcare, and security.
User control – Users should have feedback mechanisms to adjust AI behavior.
Ethics and safety – UX designers play a key role in making AI products ethical, inclusive, and secure.
Adaptability – AI should learn from user behavior and adjust to their preferences.
Building trust – AI products should be transparent, predictable, and intuitive.
Managing user expectations – UX design should create realistic expectations to avoid user frustration.
Decision transparency – Users should have access to information on how AI makes decisions.