Avoiding pitfalls of AI for designers: Guiding principles

With the AI landscape changing daily — even by the hour — how do we design for the power of AI without falling into the trap of bias, misinformation, and dubious ethics? It feels like a tall order.

One answer might be in using a set of principles that place the human at the center of our focus. These principles not only make sure that AI systems are user-friendly but also that the system is fully functional and covers the use cases we are designing for.

Key UX challenges when designing AI products

Before we begin, we need to acknowledge the unique and anxiety-inducing challenges that designers are now facing:

• User expectations — AI is not a magic bullet and users will need to understand AI’s capabilities and limitations, especially when dealing with complex and evolving product requirements. Communicating these capabilities and limitations to users accurately will help prevent confusion and misalignment with expectations
• Trust — Product design often involves sensitive data and high-stakes decisions. Building trust in the AI system is key, as users wouldn’t be willing to use a product they don’t trust with their information or to make decisions for them
• Accessibility and edge cases — User needs can be complex and also nuanced. The AI system must be able to handle edge cases and allow for diverse user needs. There has not been enough thinking that I have seen in how accessibility fits into the AI picture, but we can try and address this
• Transparency and usability — Explaining the AI’s decision making process is crucial for trust, but it shouldn’t come at the expense of usability
• Bias, misinformation, and privacy risks — AI systems are only as good as the data they’re trained on. We must be vigilant in addressing potential biases and ensuring data privacy

There are a number of guiding principles we can follow to help us address these challenges and ensure the AI products, services, and features we design keep the user front-of-mind.

Guiding principles for ethical and effective AI design

Let’s explore the principles that can guide us towards creating AI-powered product design tools that are both effective and ethical. These principles have been compiled from a number of great sources, such as IBM’s AI Design Principles, Google’s People + AI Guidebook, and Generative AI Principles from the ACM:

Even better, these have been tested with AI products already, so they provide a solid starting point — a framework you can adapt and customize as you navigate the unique contours of your own design challenges.

1. Design responsibly

AI in product design is not about replacing humans; it’s about augmenting them. Start with the user, their needs, and their frustrations, trying to understand the value that AI can bring to their workflow — such as automation, idea generation, or something more profound.

Never forget that data integrity should be uppermost in our minds. Just treat it with the respect it deserves.

How to achieve this:

• Allow users to fine tune the AI’s output
• Make sure that users can understand the AI’s reasoning
• Allow for escalation to human experts when needed
• Create and display an ethics checklist for the AI’s recommendations

2. Design for variability

A product design is not static; it evolves and builds on previous iterations. Design your AI systems to mirror this fluidity — expect multiple outputs, acknowledge the inherent variability of generative AI, and build in the capacity to adapt.

How to achieve this:

• Give users the tools to customize design templates
• Allow users to “favorite” successful strategies
• Facilitate side-by-side comparisons of designs
• Create a “sandbox” for experimentation

3. Design for mental models

Don’t get lost in the technical weeds. Instead, focus on allowing users to understand the benefits of AI, not the intricacies of its algorithms. Frame AI’s value in the context of their workflows. Most importantly, show them how it saves time, reduces errors, and displays insights that were out of reach to them.

How to achieve this:

• When onboarding users, show clear explanations of how the AI works
• Showcase some real-world examples of successful AI implementation
• Provide a familiar interface, with features like a search function for design elements

4. Design for co-creation

Product design is rarely a solo effort. Design your AI systems to foster collaboration and co-creation. Allow users to edit AI generated outputs, as well as to add their own insights and comments. When automation fails, give the user control over their work. Create a feedback loop that allows users to contribute to the ongoing improvement of the AI system.

How to achieve this:

• Enable direct editing of AI-generated designs
• Provide version control features
• Offer templates for users to customize
• Facilitate collaboration among multiple users
• Implement a “design workflow builder”

5. Design for trust

Again, let’s repeat this: creating trust is the most important aspect of our job. Users need to understand how the AI arrives at its conclusions, why it flags certain risks, and how it makes its recommendations. Explainability is not a luxury, it’s a necessity.

How to achieve this:

• Calibrate trust by providing clear explanations for the AI’s decisions
• Show users the specific data that informed the AI’s assessment
• Use visual cues to indicate its confidence level
• Provide links to relevant design resources
• Offer the option of a human review

6. Design for imperfection

AI is not infallible. Design your systems to acknowledge and handle errors gracefully. Provide clear paths forward from failure, allowing users to recover and contribute to the AI’s ongoing improvement.

How to achieve this:

• Allow users to flag incorrect information
• Suggest improvements to the knowledge base
• Report errors through feedback forms
• Use feedback and data analysis to refine the AI’s performance

7. Design for multimodal interaction

Product design is not just about text. It’s about images, diagrams, physical prototypes, and real-world environments.

---

Over 200k developers and product managers use LogRocket to create better digital experiences

Learn more →

---

Design your AI systems to embrace this multimodal reality. Go beyond text-based interaction and explore how voice commands, image recognition, or even augmented reality can enhance the user experience.

How to achieve this:

• Allow users to upload product images for compliance analysis
• Implement voice commands for hands-free interaction

Unlocking a new era: Navigating AI product design

By using these principles as a design guide, we can navigate the complex world of product design with confidence, ensuring AI serves as a tool for progress, not a source of unintended consequences. The future of design isn’t artificial intelligence; it’s augmented intelligence, a co-pilot between our creativity and AI’s power.

LogRocket helps you understand how users experience your product without needing to watch hundreds of session replays or talk to dozens of customers.

LogRocket's Galileo AI watches sessions and understands user feedback for you, automating the most time-intensive parts of your job and giving you more time to focus on great design.

See how design choices, interactions, and issues affect your users — get a demo of LogRocket today.