User trust in ambient computing

Building ambient AI: Privacy by design isn't negotiable. Last week, our Customer Advisory Board had a thought-provoking discussion on ambient computing's future. We quickly moved beyond AI capabilities to something more complex: how we unlock incredible potential while maintaining control and consent when recording becomes invisible. We're entering an era where AI-powered devices will silently capture and process our conversations in real time—Meta's Ray-Ban glasses, Limitless clips, and countless devices embedding into our physical spaces. The upside is transformative. Imagine a doctor whose smart glasses automatically document patient interactions and generate medical records while maintaining eye contact. Or ambient AI helping someone with dementia by gently providing reminders without obvious assistive technology. But here's the complexity: Today, you can see when someone's recording a Zoom call—you can ask them to turn it off. Ambient computing makes this invisible. The person across from you might be wearing smart glasses feeding your conversation to an AI model, with no clear consent mechanism or easy opt-out. This creates profound challenges. How do you have confidential conversations when you can't tell what's listening? What happens to attorney-client privilege, medical consultations, or personal moments when ambient AI is everywhere? How do we maintain the spontaneity and trust that relationships require? The stakes extend beyond individual privacy: cross-contamination of sensitive data, AI making decisions based on overheard conversations, and the chilling effect on free expression when every word might be recorded and analyzed. We need privacy by design principles embedded at the hardware level—like how drones are geofenced from airports. Data minimization, voice de-identification, and automatic redaction should be default. Should society create "privacy zones" where ambient devices are automatically disabled? The blueprint for humane ambient computing is on the table. The clock is ticking on whether we build with it, or let the cement set on a world where every whisper is captured by default.

User Trust Is the New UX Currency When users stop trusting your product, no design system, onboarding flow, or AI model can save it. Trust isn’t built with pixels — it’s built with predictability, transparency, and accountability. In my research on Human-AI collaboration at Intel Labs, I’ve seen this pattern repeatedly: - Users forgive imperfection but not deception. - They value transparency over magic. - And the fastest way to lose them is when the system behaves unpredictably — even if it’s technically correct. Building trust in AI-driven products means shifting our mindset: - From “Can the model do it?” → to “Can the user trust how it does it?” - From accuracy metrics → to trust metrics (perceived control, reliability, fairness). - From explanations → to shared understanding. Because the most responsible AI products aren’t the ones that automate the most — they’re the ones that users feel safe to rely on. So as UX researchers, we’re not just measuring usability anymore. We’re measuring trustworthiness. And that’s what will define the next generation of ethical, human-centered AI experiences. #UXResearch #AIUX #ResponsibleAI #HumanAICollaboration #UserTrust #UXDesign #EthicalAI

🌻 Designing For Trust and Confidence in AI (Google Doc) (https://smashed.by/trust), a free 1.5h-deep dive into how trust emerges, how to design for autonomy, risk, confidence, guardrails — with all videos, slides and examples in one single place. Share with your friends and colleagues — no strings attached! ♻️ Google Doc (slides, videos, links): https://smashed.by/trust All slides (PDF): https://lnkd.in/dsq2BAJJ Full 1.5h-video recording: https://lnkd.in/d72b66Qa Zoom video backup: https://lnkd.in/dZJzCnZh Key takeaways: 1. Trust doesn’t emerge by default — it must be earned. 2. Trust means strong believing, despite uncertainty. 3. It’s when system is competent, predictable, aligned. 4. It also means transparency about its limitations / capabilities. 5. AI feature retention often plummets due to lack of confidence. 6. Trust isn’t linear: takes time to be built, drops rapidly in failures. 7. Most products don’t want users to fully rely on them → complacency. 8. Trust requires Understanding + Success moments + Habit-Building. 9. It thrives at intersection of Perceived value + Low cognitive effort. 10. We need to “calibrate” trust to avoid over-reliance and aversion. 11. Transparency only builds trust if users can verify the output. 12. User must feel in control: to validate, shape and override output. 13. Users have low tolerance for mistakes if AI acts on their behalf. 14. High-autonomy + High-risk → human intervention is non-negotiable. 15. Start with human oversight, increase autonomy as trust grows. 16. Perceived usefulness + ease of use are primary drivers of AI adoption.  17. Biggest risk to effort is a blank page → leads to open-intent paralysis. 18. Confidence builds through frequent use, not through “blind” trust. 19. Confidence scores are insufficient to help people make a decision. 20. AI might absorb cognition, but humans inherit the responsibility. Design patterns: 1. Link to specific fragments, not general sources. 2. Show the distribution of opinions, not a final answer. 3. Use structured presets to help articulate complex intents. 4. Rely on buttons/filters for a precise control or tweaking. 5. Show sandbox previews to help understand outcomes. 6. For high-stakes scenarios, design approval steps and flows. 7. Explicitly label the assumptions made during processing. 8. Replace confidence scores with actions, requests for review. 9. Embed AI features into existing workflows where work happens. 10. Proactively ask for context around the task a user wants to do. 11. Reduce effort for articulation with prompt builders/tasks. Recorded by yours truly with the wonderful UX community last week. And a huge *thank you* to everybody sharing their work and their findings and insights for all of us to use. 🙏🏼 🙏🏾 🙏🏾 ↓

Your AI doesn't need a higher IQ. It needs a better bedside manner. 🧠🤝 We talk a lot about "Model Accuracy," but we don't talk enough about "User Trust." In our recent F500 engagement, the biggest hurdle wasn't the math—it was the UX. Users are naturally skeptical of "Black Box" logic. If the AI says "No" and doesn't explain why, the user abandons the tool. millermedia7 (M7) built the AI Design System to solve this: Reasoning Skeletons: Visual cues that show the AI "thinking" through compliance. Validation Badges: Proving the data was cross-referenced against a governed registry. Turn a "Scary Algorithm" into a "Trusted Partner. AI is a trust problem, not a technical one. If your users don't understand how the AI reached its conclusion, they will never adopt it at scale. #AIdesign #UX #ProductDesign

The hidden metric in AI adoption? User trust. Users who understand how an AI system works and feel in control are more likely to embrace it. It's more than having powerful features—it's about making those features accessible and understandable. The framework we use: - Transparent AI decision pathways - User data sovereignty - Built-in feedback mechanisms - Strategic human oversight and testing - Incremental deployment methodology The formula works: AI Transparency + Control = Increased Adoption How are you measuring trust in your AI initiatives? #AI #DigitalStrategy #Innovation #adoption