Explaining AI Concepts

Imagine you’ve just built an incredible AI system. It can analyze images with stunning accuracy, predict customer behavior with remarkable precision, and generate text that sounds almost human. It’s a technological marvel… that nobody understands.

Sound familiar? You’re not alone. The gap between what AI systems can do and what everyday users understand is one of the biggest challenges in AI adoption today.

In this module, we’ll explore the art and science of making AI understandable, usable, and trustworthy for the people who matter most: the humans who use it. Whether you’re writing UI text, creating help documentation, or designing onboarding experiences, you’ll learn practical strategies to bridge the gap between AI complexity and human understanding.

The Great Translation Challenge

Here’s a confession that might make some AI engineers uncomfortable: Most end users don’t care about your neural network architecture. They don’t lie awake at night wondering about your model’s activation functions or hyperparameter tuning strategy.

What they do care about is much more practical:

1. What can this AI do for me? (capabilities)
2. How well does it work? (and when might it fail)
3. How do I use it effectively? (instructions)
4. Is my data safe? (privacy concerns)
5. Why did it make this decision? (explainability)

Your job as an AI explainer is to translate complex technical realities into meaningful human terms—without sacrificing accuracy or oversimplifying to the point of deception.

Understanding Your User’s Mental Model

Before you write a single word explaining your AI system, take a moment to consider your user’s mental model—how they think the world works.

Mental models are the simplified internal representations we all carry around to make sense of complex systems. They don’t need to be technically accurate; they just need to be useful for making predictions and decisions.

For example, most people don’t understand how their car’s engine works at a mechanical level, but they have a functional mental model: “I put in gas, press the pedal, and it goes.”

The Mental Model Gap

The challenge with AI is that user mental models often don’t align with technical reality:

What users might think: “The AI reads my emails and understands them like a person would.”

Technical reality: “A language model processes text through contextual embeddings, attention mechanisms, and statistical pattern matching without semantic understanding.”

Your explanation needs to bridge this gap while respecting both the technical truth and the user’s existing understanding.

Finding the Right Level

Different users require different explanations. Consider these contrasting mental models for the same recommendation system:

Technical user: “This probably uses collaborative filtering combined with content-based features to create embeddings of my preferences in a latent space.”

Everyday user: “It shows me stuff similar to what I’ve liked before, and things that people like me have enjoyed.”

Both are valid ways to think about the system, but the language and concepts you use should match your audience’s existing knowledge and needs.

The Art of Layered Explanations

One of the most effective strategies for explaining AI is to create layered explanations that allow users to dig as deep as they want to go.

Start with a simple, one-sentence explanation that everyone can understand. Then provide options to learn more, adding technical detail progressively for those who are interested.

Example: Layered Explanation for Image Recognition

Level 1 (Basic): “Our app recognizes what’s in your photos so you can find them easily later.”

Level 2 (More Detail): “The app analyzes patterns of color, shape, and texture to identify objects, people, and scenes in your photos, making them searchable by content.”

Level 3 (Technical): “Our image recognition uses a convolutional neural network trained on millions of labeled images to classify visual content with over 90% accuracy for common objects and scenes.”

This approach respects different users’ needs and curiosity levels without overwhelming anyone.

Where and When to Explain AI

The best AI explanations appear at the moment users need them—not too early, not too late. Here are the key moments to provide explanations:

1. During Onboarding and First Encounters

When users first meet your AI feature, briefly explain:

• What it does (its superpower)
• What value it provides (why they should care)
• How it learns or adapts (if applicable)
• What control they have (how to manage it)

Good example:

Welcome to Smart Reply! 

I'll suggest quick responses based on emails you receive. These suggestions are designed to save you time on common replies.

The suggestions will improve as you use them. Just tap a suggestion to use it, or type normally to ignore.

This explanation focuses on value (“save you time”), sets expectations (“common replies”), and explains control (“tap to use or ignore”).

2. In Context Within the UI

Subtle explanations in the interface provide just-in-time context without interrupting flow:

• Tooltips that appear on hover
• Labels indicating AI-generated content
• Confidence indicators showing certainty levels
• Attribution showing where information comes from

Good example:

[TOOLTIP ON HOVER]
These results are personalized based on your past searches and purchases. You can adjust your personalization settings in your account.

3. When Something Unexpected Happens

AI systems sometimes behave in ways users don’t expect. These moments require immediate explanation:

Good example:

We couldn't find an exact match for "barista coffe maker."
Showing results for "barista coffee maker" instead.
We made this correction based on common spelling patterns.

This explains what happened, what the system did about it, and how it made that decision.

4. In Help Documentation and Support Materials

Dedicated help content allows users to learn more when they want to:

• How-to guides for task-focused instructions
• FAQ sections answering common questions
• Conceptual explanations of how the technology works
• Troubleshooting guides for when things go wrong

The AI Explainer’s Toolkit

Different explanation challenges require different approaches. Here are six powerful methods for making AI understandable:

1. Metaphors & Analogies

Connect unfamiliar AI concepts to familiar concepts from everyday life.

Instead of this:

Our natural language processing system uses an attention-based neural network architecture to interpret semantic relationships.

Write this:

Think of our writing assistant as a student who has read millions of books and learned language patterns. It's similar to predictive text on your phone, but much more sophisticated.

Good metaphors create an “aha!” moment of understanding. Just be careful not to choose metaphors that imply the AI has human-like understanding or consciousness.

2. Concrete Examples

Examples are often more effective than abstract explanations because they show the AI in action in a relatable context.

Instead of this:

The image recognition system identifies objects and their relationships in photographs using visual pattern recognition.

Write this:

When you upload a vacation photo, the system can identify things like "person swimming in a lake with mountains in the background" rather than just "person" or "lake." This lets you find specific photos by searching for what's happening in them.

Examples help users visualize the practical applications of the technology.

3. Visual Explanations

Some concepts are simply more understandable when shown visually:

• Charts showing how confidence levels affect decisions
• Diagrams illustrating how data flows through a system
• Animations demonstrating cause and effect
• Interactive visualizations showing “what if” scenarios

4. Progressive Disclosure

Start with the simplest explanation, then allow interested users to “dig deeper” for more details:

First level (for everyone):

Your feed shows posts we think you'll find interesting.

Second level (for curious users):

We select posts based on several factors: accounts you follow, posts you've engaged with, and topics you seem to enjoy.

Third level (for detail-oriented users):

Your feed is personalized using several signals: accounts you follow (weighted by how often you engage with them), topics you've shown interest in, content similar to posts you've liked or shared, and some newer content to help you discover new interests.

5. Counterfactual Explanations

Explain how changing inputs would change the AI’s output:

You weren't approved for this loan amount because:
• Your debt-to-income ratio is 45% (we typically approve when it's under 40%)
• Your credit history is 2 years (we typically look for 3+ years)

If either of these factors changed, you might qualify for a different amount.

This approach helps users understand key decision factors and what they might do differently.

6. Interactive Experiences

Let users experiment with inputs to see how outputs change:

Move the sliders to adjust your priorities:
• Speed vs. Scenic route
• Cost vs. Convenience
• Walking distance vs. Door-to-door

Interactive explanations create “learning by doing” which can be more effective than passive reading.

The Confidence Challenge: Explaining Uncertainty

One of the trickiest aspects of explaining AI is communicating uncertainty. Most AI systems don’t produce binary yes/no answers—they generate probabilities or confidence scores.

The problem? Most humans aren’t natural statistical thinkers.

Making Confidence Meaningful

Here are strategies for explaining confidence and uncertainty:

1. Use visual indicators like colors (red/yellow/green) or icons
2. Match confidence levels to appropriate actions:
   • High confidence → Take action automatically
   • Medium confidence → Suggest action with qualification
   • Low confidence → Ask for confirmation
3. Adjust language based on certainty:
   • High: “This is a dog”
   • Medium: “This appears to be a dog”
   • Low: “This might be a dog”
4. Provide context for numbers:
   • “90% confidence (very certain)”
   • “65% confidence (somewhat uncertain)”

When explaining confidence, focus on what it means for the user’s decision-making, not the statistical details.

Principles for Honest and Ethical AI Explanations

As AI explainers, we have an ethical responsibility to be honest about what systems can and cannot do.

1. Be Honest About Capabilities and Limitations

Don’t oversell what your AI can do. Set appropriate expectations:

Instead of this:

Our AI perfectly understands your writing style and will write emails exactly as you would.

Write this:

Our AI suggests completions based on your previous emails and common phrases. It works best for standard greetings and common expressions, but you should always review suggestions for accuracy and tone.

2. Clarify What’s AI vs. Human

Users have a right to know when they’re interacting with AI versus humans:

Good example:

[AI-GENERATED SUMMARY]
This summary was created automatically and may miss nuance or context. Read the full article for complete details.

3. Explain Data Usage Clearly

Be transparent about how user data is used:

Good example:

To personalize your experience, we use:
• Your past interactions with similar content
• Preferences you've set in your account
• General trends from users similar to you (anonymized)

Your data is never shared with other companies or used to identify you personally.

4. Focus on User Benefit, Not Technical Impressiveness

Explain what’s in it for the user, not how clever your AI is:

Instead of this:

Our state-of-the-art deep learning algorithm processes millions of data points to generate recommendations with unprecedented accuracy.

Write this:

You'll discover new songs you'll love without having to search for them, saving you time and introducing you to artists you might have missed.