Explainable AI (XAI)
What is Explainable AI (XAI)?
Explainable AI (XAI) is a design pattern that makes AI decisions understandable by showing how and why the system reached its conclusions. Instead of treating AI as a mysterious black box, this pattern uses visualizations, natural language explanations, and transparent reasoning to build trust and enable verification. It's essential for high-stakes decisions like medical diagnosis or loan approvals, debugging AI systems, or any application where users need to understand the logic behind recommendations. Real examples include Claude showing step-by-step thinking, Perplexity citing sources for every claim, or credit scoring systems explaining which factors influenced your score.
Problem
AI systems often act as 'black boxes,' hindering understanding of decisions. This reduces trust, complicates debugging, and allows biased or incorrect decisions to go unnoticed.
Solution
Explain AI conclusions using visualizations, natural language, and interactive elements. Help users understand reasoning, data sources, and confidence levels.
Real-World Examples
Implementation
Figma Make Prompt
Guidelines & Considerations
Implementation Guidelines
Provide explanations at appropriate detail levels for different user types.
Use visual aids (heatmaps, charts, diagrams) to illustrate decision factors.
Show confidence levels and uncertainty ranges for AI predictions.
Explain both what and why the AI decided.
Provide source attribution when applicable.
Use natural language explanations for non-experts.
Allow users to drill down for more detailed explanations.
Show alternative options considered but not chosen.
Highlight the most important factors influencing the decision.
Design Considerations
Balance explanation detail with cognitive load and usability.
Consider different explanation needs for varying expertise levels.
Ensure explanations are accurate without oversimplifying.
Account for cases where AI reasoning is too complex for simple explanations.
Consider privacy implications of showing detailed decision factors.
Plan for scenarios where explanations might reveal system vulnerabilities.
Test explanations with real users to ensure helpfulness.
Consider cultural and linguistic differences in explanation preferences.
Balance transparency with intellectual property protection.