Designing for explainability is a critical aspect of creating products, systems, and technologies that users can understand and trust. Yet, many attempts at explainability fall short because they rely heavily on jargon—technical terms, buzzwords, or complex language that alienates or confuses users instead of clarifying the underlying concepts. Effective explainability demands clear communication that is accessible to diverse audiences, regardless of their technical background.
To achieve this, the design process must prioritize simplicity, transparency, and user empathy. Here’s a detailed exploration of how to design for explainability without jargon:
1. Understand Your Audience
Before crafting any explanation, it’s vital to understand who the users are, what their knowledge level is, and what they care about. Different audiences require different approaches:
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Non-expert users: Need straightforward, relatable explanations.
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Domain experts: Can handle more technical details but still benefit from clarity.
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Mixed audiences: May require layered explanations or adjustable levels of detail.
Knowing your audience helps avoid unnecessary jargon and focuses on language that resonates and informs.
2. Use Everyday Language and Analogies
Replace technical terms with simple, everyday language. When technical terms are unavoidable, explain them using analogies or metaphors. For example, describing a complex algorithm as “a recipe that follows a step-by-step process to mix ingredients for the best outcome” can make abstract ideas more tangible.
3. Break Down Complex Concepts into Smaller Steps
Complex processes or systems can overwhelm users if presented all at once. Breaking explanations into smaller, manageable pieces allows users to digest information incrementally. This method aligns with cognitive load theory, which suggests that people learn better when information is chunked logically.
4. Visual Aids and Interactive Elements
Visual explanations—such as diagrams, flowcharts, or animations—can clarify difficult concepts more effectively than text alone. Interactive elements allow users to explore different parts of a process at their own pace, helping them build a deeper understanding without feeling rushed or lost.
5. Focus on the “Why” and “How”
Explainability is more than describing what a system does; it’s about clarifying why and how decisions are made. For example, in AI systems, users are more likely to trust outcomes if they understand the factors influencing the decision, not just the final result. Present these reasons clearly and avoid abstract technical descriptions.
6. Prioritize Transparency Over Complexity
Transparency means openly sharing relevant information about how a system works without overwhelming the user with unnecessary technical details. For instance, instead of detailing every mathematical equation behind a recommendation engine, explain that it considers recent user behavior and preferences to tailor suggestions.
7. Use Consistent Terminology
Even when avoiding jargon, consistency in the terms used is crucial. Switching between different words for the same concept can confuse users. Define terms clearly early on and use them uniformly throughout the explanation.
8. Design for Feedback and Questions
Allow users to ask questions or provide feedback about explanations. This interaction helps identify which parts are unclear and offers an opportunity to refine the communication. For example, incorporating tooltips, FAQs, or chatbots that clarify terms on demand can enhance understanding.
9. Test Explainability with Real Users
No explanation is truly effective until tested. Usability testing with target users can reveal jargon that slipped through or concepts that remain confusing. Iterative testing and revision ensure explanations meet real-world needs.
10. Balance Detail with Brevity
While detailed explanations can be helpful, too much information can be overwhelming. Aim for concise, relevant explanations that answer key questions without drowning users in excess detail. Layered explanations, where users can “drill down” for more info if they choose, are particularly effective.
Practical Example: Explaining a Credit Score System
Instead of saying:
“Our system uses a proprietary algorithm that evaluates creditworthiness based on FICO scores, debt-to-income ratios, and payment history using machine learning models.”
Use:
“We look at your past payments, how much debt you have compared to your income, and other simple factors to decide your credit score. Think of it like a report card that shows how well you manage your money.”
Visual aids could show a simple chart with these factors and how they contribute to the score. Interactive elements might allow users to see how changing one factor, like making payments on time, improves their score.
Designing for explainability without jargon is a powerful way to build user trust and engagement. It respects users’ time and intelligence, helps avoid misunderstandings, and ultimately leads to better adoption of technologies and products. Clear, accessible explanations are not just good communication—they are essential design features that foster transparency, accountability, and inclusivity.
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