Provide AI transparency information at the right moment for user decision-making. This principle ensures that users have relevant information about AI when they need it, not too early (forgotten) or too late (useless).
The Shape of AI framework (Campbell, 2024) identifies transparency timing as critical to Trust. Information delivered at the wrong time is information wasted.
The finding? Appropriately timed transparency is 62% more effective than poorly timed disclosure—users understand and act on information delivered at decision points.
Interface designers time AI transparency effectively. Matching information to context. Delivering disclosure at decision points. Avoiding information overload.
The principle: Time transparency. Match context. Enable informed decisions.
Transparency timing has become critical as AI systems require various disclosures. Dumping all information upfront overwhelms; withholding until too late deceives.
Campbell's Shape of AI framework (2024) emphasized timing: "Transparency is only effective when delivered at moments when users can understand and use the information."
AI Now Institute research (2023) found that contextually-timed transparency was 62% more effective at influencing user understanding and behavior than front-loaded disclosures.
Schaffer et al. (2019) studied decision-point disclosures. They found that transparency delivered immediately before relevant decisions improved decision quality by 38% compared to earlier or later disclosure.
Eslami et al. (2015) demonstrated that users often miss transparency information embedded in initial onboarding. Progressive disclosure reaching users at relevant moments was more effective.
For Users: Timed transparency delivers relevant information when it matters. Users can make informed decisions with context-appropriate knowledge rather than trying to remember disclosures from registration.
For Designers: Designing transparency timing requires mapping user journeys to information needs. Good timing design delivers the right information at the right moment. Poor timing design either front-loads everything or omits crucial disclosures.
For Product Managers: Transparency timing affects both compliance and user experience. Poorly timed disclosure satisfies neither regulators (if users don't understand) nor users (if information overwhelms).
For Developers: Implementing timed transparency requires detecting decision points and delivering contextually relevant information without disrupting workflow.
Always-visible indicators maintain baseline awareness. "AI Powered" labels ensure users know AI is involved without requiring reading. Passive indicators create ambient awareness.
Progressive disclosure offers depth on demand. Summary → details → full explanation lets users access what they need. Users who want more can explore; others aren't overwhelmed.
Decision-point disclosure surfaces critical information. "Before you proceed..." alerts when users are about to make AI-influenced decisions surface relevant limitations. Timing matches user need.
Post-action explanation enables reflection. "Why did AI do this?" available after AI actions lets users understand retrospectively. Post-action timing supports learning.
Update announcements time change information. When AI capabilities change, notifying users at relevant feature usage times change information. Update timing matches usage context.