Trust by Design: Building Reliable AI for Sensitive Environments
summary
Explains a tested workflow that embeds a human-in-the-loop participatory design process to develop LLM interfaces, benchmarks and continuous evaluation; delivers a concrete framework with 5 trust areas, 15 dimensions tying data quality, interface cues and model behavior to ethics and compliance; shows UI patterns that surface sources (Perplexity-style), present probability indicators for answers, and modulate language complexity by user AI literacy; argues these measures reduce hallucinations and calibrate trust beyond mere AI Act stickers.
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chapters (7)
- 0:00Trust in AI systems
- 2:06Trust enables AI adoption
- 3:50Human interaction with AI
- 4:52Human-in-the-loop for trustworthy AI
- 7:41Trust calibration and AI literacy
- 10:54Ethics and trust in AI
- 13:50Takeaways and future
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