Model metrics in explanations

Model metrics can explain AI outcomes, but technical numbers confuse users. The solution is translating these measurements into everyday language that helps people make decisions.

A document search showing "0.73 cosine similarity" means nothing to most users. But "73% relevant to your search" makes immediate sense. Movie apps do this well by showing "85% match" instead of complex filtering scores.

Generative AI needs different approaches since single accuracy numbers don't capture quality. A coding assistant might create "helpfulness scores" based on developer feedback. Seeing "92% helpful" gives users meaningful context about whether to trust a suggestion.

Choose metrics that connect to user goals. News apps benefit from "freshness scores." Dating apps use "compatibility percentages." But showing "perplexity values" for text generation helps no one. Visual elements improve understanding. Stars, progress bars, and color coding communicate better than numbers alone. Keep these consistent across your product so users learn what to expect.