Adapting trust indicators to user expertise

Different users need different trust signals. Expert users and beginners interpret confidence displays differently. What helps one group might confuse or mislead another. Novice users often misunderstand percentages. Showing "87% confidence" assumes users know this is high for AI predictions. They might expect 100% and see anything less as failure. Simpler indicators like "Very likely" or showing top alternatives work better for general audiences. Domain experts want detailed information. A doctor reviewing AI diagnosis suggestions needs confidence scores, alternative possibilities, and data sources. They can interpret "differential diagnosis with 73% posterior probability" because they use similar concepts daily. This technical depth would overwhelm patients. Context determines complexity needs. The same user might want simple guidance for entertainment choices but detailed analysis for financial decisions. A movie recommendation needs just stars or thumbs. An investment suggestion needs confidence intervals and risk assessments.

Cultural backgrounds affect trust interpretation, too. Some cultures prefer collective validation ("9 out of 10 similar users agreed") while others trust individual metrics. Number-heavy displays might signal reliability in one culture but seem cold in another.