What is XAI?
Explainable AI (XAI) refers to techniques that make AI systems understandable to humans. Instead of being a “black box,” models provide explanations for their predictions or rankings.
How It Works (quick)
- Local methods: Show why one result was chosen (e.g., SHAP, LIME).
- Global methods: Summarize overall model behavior.
- Search: Highlight matched fields, boosts, and weights.
- UI: Explain why products appear (“Matched on color: blue, boosted by popularity”).
Why It Matters in E-commerce
- Builds trust in search and recommendation engines.
- Helps merchandisers fine-tune ranking rules.
- Improves compliance with AI regulations (EU AI Act, GDPR).
Best Practices
- Provide human-readable explanations (“Matched title and description”).
- Keep explanations concise in the UI.
- Use visual highlights (bolding, icons).
- Balance transparency with security/business confidentiality.
Summary
XAI makes AI-driven search transparent. In e-commerce, it builds shopper trust and helps teams tune algorithms responsibly.
