How to Design AI Explanations Users Understand
AI can feel complex, but clear explanations make it easier for users to trust and use effectively. Here’s how to design AI explanations that anyone can understand:
Beginners
Simple guides, no jargon
Developers
Technical specifics, APIs
Executives
High-level insights, ROI
Know Your Users' AI Experience Level
Understanding how familiar your users are with AI helps you create content that’s clear, engaging, and encourages adoption. Start by organizing your audience into groups to deliver content that meets their specific needs.
Map User Groups
Different groups will have varying levels of technical expertise:
User Group
AI Knowledge Level
Primary Needs
Executives
Strategic understanding
High-level insights on impact and ROI
Product Managers
Intermediate
Practical use cases and implementation steps
Developers
Advanced
Detailed system architecture and API guides
End Users
Basic or none
Easy-to-follow guides and instructions
Match Content to Skill Levels
Beginner Level
Intermediate Level
Advanced Level
Keep your content structured and provide options for users to explore more detailed information when needed.
Make AI Concepts Easy to Understand
Break down technical terms into simple, everyday language while keeping the meaning intact.
Write in Plain Language
Here are some ways to explain AI terms more simply:
Technical Term
Plain Language Alternative
Example Usage
Neural Network
Pattern Recognition System
"Works like a brain that learns by studying examples."
Algorithm
Step-by-step Process
"A recipe the computer follows to solve problems."
Automated Learning
"The system gets better by learning from experience."
Data Analytics
Pattern Finding
"Finding useful trends in information."
When discussing AI, focus on how it affects people rather than diving into the technical details. For example, instead of explaining the complex math behind natural language processing, say: "The AI reads your email and suggests quick responses based on how you’ve replied in the past."
Once you've simplified the terms, use relatable examples to make the concepts even clearer.
Show Examples That Make Sense
Connect AI's capabilities to everyday situations to make the technology feel more approachable.
"They take the time to understand our company and the needs of our customers to deliver tailored solutions that match both our vision and expectations. They create high-quality deliverables that truly encapsulate the essence of our company." - Isabel Sañez, Director Products & Operations
Here are some relatable comparisons:
Show AI Processes Through Pictures
Visual aids can make AI processes easier to understand by breaking down abstract ideas into clear, relatable workflows. Diagrams and interactive tools can help users feel more confident by showing how AI systems work.
Create Clear Diagrams
When designing diagrams to explain AI processes, keep the user's perspective in mind. Aim for simplicity and clarity, ensuring the visuals align with the audience's knowledge level. Here are some tips:
For example, you could depict an AI recommendation system with a simple flow: User Input → AI Processing → Personalized Results. Add the option to explore technical details for those who want to dive deeper.
Static visuals like these can be paired with interactive tools to enhance understanding.
Add Interactive Elements
Interactive visualizations allow users to engage with AI systems and see how they work in real time. This hands-on approach can make even complex processes feel approachable.
Consider adding features like:
To design effective interactive tools, keep these principles in mind:
The goal is to create visuals and tools that accurately explain AI without oversimplifying. Every diagram and interactive feature should help users better understand how AI makes decisions.
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Be Open About How AI Works
When it comes to AI, being upfront about how it operates is key to earning user trust. People need to know what your AI can and can't do, as well as how it handles their data. Clear communication sets the right expectations and helps users feel confident in using your system.
State What AI Can't Do
Let users know the limits of your AI. This avoids unnecessary frustration and increases credibility. Here's how to approach it:
For instance, if your chatbot can't analyze images, say so plainly: "This assistant is designed for text-based interactions only. For image analysis, please use our image recognition tool." This kind of clarity helps users understand what to expect and where to turn for other needs.
Explain Data Handling
Trust also hinges on how you manage user data. Be clear about what happens to their information by addressing these points:
Data Aspect
What Users Should Know
Collection
Types of data gathered and how it's collected
Usage
How the AI processes data to generate results
Protection
Security measures in place to safeguard their privacy
Control
Options for users to manage or delete their data
Offer Different Detail Levels
Providing explanations at varying levels of detail ensures users can engage with information at a pace that works for them. People have different needs when it comes to understanding AI systems, so it's important to cater to both casual users and those who want in-depth insights.
Start With the Basics
Begin by clearly explaining the essentials:
Information Level
What to Include
Example
Core Function
Main purpose
"This AI helps write emails."
Basic Benefits
Key advantages
"Saves time and reduces errors."
Simple Process
Basic workflow
"Type your request, review suggestions."
Offer Additional Details
For users who want more depth, provide extra information through features like progressive disclosure, links to technical documentation, or embedded tooltips. These tools can reveal detailed insights on demand, allowing users to explore as much - or as little - detail as they need.
Here’s how you can layer explanations for complex features:
1. Basic: A short overview. Example: "The AI analyzes your writing style to suggest similar phrasing."
2. Intermediate: A slightly deeper explanation. Example: "It learns from your previous documents to match your tone and vocabulary preferences."
3. Advanced: Technical details for those who want a full breakdown. Example: "The system uses natural language processing models trained on a large dataset to identify and replicate stylistic patterns."
This step-by-step approach makes it easier to address the needs of both beginners and advanced users, complementing earlier strategies for tailoring content to different expertise levels.
Steps to Better AI Explanations
Creating clear, user-focused explanations is crucial for encouraging AI adoption. The goal is to make AI systems understandable while fostering trust.
Here’s a useful framework for designing AI explanations:
Layer
Purpose
How to Implement
Foundation
Basic understanding
Use plain language to describe core functions
Engagement
Build confidence
Include interactive features and visual aids
Transparency
Foster trust
Clearly explain data handling and limitations
Accessibility
Accommodate users
Offer progressive details for varying needs
This structure ensures explanations are simple yet detailed enough to cater to different users. As Dr. -Ing. Jens Popper, CPO, puts it:
"Their team showed an incredible learning mindset as well as a high level of creativity and collaboration. The end result is beautiful and deceptively simple, which is incredibly hard to achieve."
To implement this effectively, consider these steps: