Test for trust and transparency

Ten tests to test trust in AI-driven systems

1. Test for Understanding, Not Just Functionality

Ask users to explain why they think the AI made a specific decision or suggestion.

If they can’t, transparency may be too low.

Use think-aloud sessions to surface confusion or misplaced confidence.

Prompt example: “What do you think the system considered when showing this result?”

2. Measure Perceived Transparency

Include short survey items or interviews after tasks:

  • “I understand how this output was generated.”

  • “I know what data this system used.”

  • “I can tell when the system might be wrong.”

Low agreement = low transparency.

3. Probe for Trust Calibration (Not Blind Trust)

You don’t want users to always trust the AI — you want appropriate trust.

  • Present deliberately flawed or ambiguous outputs.
  • Observe whether users challenge or question them.

Red flag: Users accepting everything without hesitation.

4. Check Explainability Design

Test different explanation styles:

  • Visual (confidence bars, example highlights)
  • Textual (“The system prioritised X because Y”)
  • Interactive (expandable “why” panels)

Ask which ones help users feel informed without overload.

5. Evaluate Perceived Fairness and Bias

Show users examples across demographics, regions, or contexts.

Ask:

  • “Does this feel equally fair across groups?”
  • “Would you trust it if you were in that group?”

Use your Bias Buster cards (link) as prompts here.

6. Test Transparency Through Interaction

Trust often emerges over time.

  • Run multi-session tests to see how user confidence evolves.
  • Track whether explanations increase or decrease trust after repeated use.

7. Combine Qualitative and Quantitative Data

  • Qualitative: interviews, open-ended reflections, observed reactions.
  • Quantitative: trust scales (e.g., Jian et al. “Trust in Automation” scale), task completion confidence, opt-out rates.

8. Prototype Transparency Early

  • Don’t wait for a final AI model — mock up explanations, disclaimers, or “why” features early.
  • Users can tell you what level of detail builds trust before you code it.

9. Co-Design with Users

Let users help shape how transparency looks and feels.

  • Ask them what they want to know before trusting AI.
  • Involve them in defining “enough information to feel safe.”

10. Document and Share Findings

Capture what worked and what didn’t in building trust.

  • Summarise as “trust design patterns” for future AI projects.
  • Feed back insights to data scientists, not just designers.

See also...