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:

Know Your Audience: Tailor your content based on user familiarity with AI. Beginners need simple guides, while developers require technical details.
Simplify Language: Replace technical jargon with plain terms. For example, "Neural Network" becomes "Pattern Recognition System."
Use Visuals: Diagrams and interactive tools help explain processes like decision-making or data handling.
Be Transparent: Clearly state what the AI can and cannot do, and explain how user data is collected, used, and protected.
Layer Information: Start with the basics, then offer deeper insights for users who want more detail.

User Group	Needs
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

Focus on practical outcomes and real-world benefits.
Use simple analogies to explain concepts.
Skip technical jargon.
Include visuals to aid understanding.

Intermediate Level

Cover basic AI concepts and workflows.
Explain core processes in relatable terms.
Relate to familiar technology concepts.

Advanced Level

Dive into technical specifications, system architecture, model parameters, and API documentation.

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."
Machine Learning	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:

Predictive Analytics
Think of it like weather forecasting. Just as meteorologists use past weather patterns to predict tomorrow's weather, AI analyzes historical data to predict trends in your business.
Image Recognition
It’s similar to how we recognize faces. Just as you identify a friend by their features, AI learns to spot patterns in images after seeing enough examples.
Natural Language Processing
Imagine learning a new language. Just like people improve through conversations, AI gets better at understanding language by processing millions of text samples.

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:

Use flowcharts to map out decision paths.
Include input/output examples alongside process flows.
Apply color coding to highlight different AI functions.
Gradually reveal more details to avoid overwhelming users.

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:

Confidence sliders to adjust AI thresholds.
Input modification tools to see how outputs change.
Step-by-step walkthroughs of decision processes.
Before/after comparisons to show the impact of parameter tweaks.

To design effective interactive tools, keep these principles in mind:

Start with simple interactions to build trust.
Provide clear, immediate feedback for user actions.
Include reset options so users can easily start over.
Use tooltips to clarify technical jargon.

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:

Highlight tasks the AI isn't designed to handle.
Give simple reasons for these limitations.
Suggest alternative tools or solutions when the AI falls short.

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:

What data is collected: Specify the types of data your AI gathers and how it's collected.
How the data is used: Explain how the system processes this information to deliver results.
Security measures: Outline how you protect user information.
User control options: Let users know how they can manage or opt out of data collection.

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:

Core Function: What does the AI do? (e.g., "This AI drafts emails.")
Key Benefits: Why is it useful? (e.g., "Saves time and reduces errors.")
Simple Workflow: How does it work? (e.g., "Type a request and get suggestions.")

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:

User-First Design: Tailor explanations to your audience’s technical knowledge and needs.
Visual Clarity: Use visuals like diagrams or interactive tools to illustrate AI processes.
Transparent Communication: Be honest about what your AI can and cannot do.
Layered Information: Let users explore detailed explanations at their own pace.