Trust & Transparency
Weight: 1.2ΓConfidence calibration, source attribution, capability disclosure, decision explanation
A systematic framework for evaluating AI agents across trust, usability, accessibility, and compliance dimensions β combining NIST RMF, EU AI Act, Microsoft HAX, Nielsen Heuristics, and WCAG into one actionable toolkit.
How do you evaluate AI when the rules keep changing?
Traditional UX evaluation methods weren't designed for systems that learn, produce probabilistic outputs, and operate autonomously. When an AI agent's behavior isn't deterministic, how do you test for clarity? When the system adapts over time, how do you audit trust?
Existing frameworks address pieces of the puzzle β Microsoft HAX covers human-AI interaction, Nielsen heuristics assess usability, WCAG ensures accessibility β but none provide a unified evaluation approach for modern AI agents.
This framework synthesizes 12+ established guidelines into 6 evaluation dimensions, each with weighted criteria and severity levels for prioritized recommendations.
Confidence calibration, source attribution, capability disclosure, decision explanation
System status, recognition over recall, consistency, conversational turn-taking
Error clarity, repair strategies, human escalation, graceful degradation
Screen reader support, keyboard navigation, cognitive load, multi-modal I/O
Risk classification, harm prevention, data privacy, audit trails, bias mitigation
Human-in-the-loop, override mechanisms, scope boundaries, feedback loops
Enterprise AI agent platform for building custom agents that connect to company data.
Use this interactive tool to evaluate any AI agent. Your progress is saved automatically.
I help teams audit AI experiences using structured frameworks, turning complex requirements into actionable design improvements.