Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
2Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2382960351
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Shutong Fan (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Human Ai Collab: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Other: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| Carefully framed LLM explanations can keep users trusting wrong answers: in a randomized study of 200+ people, adversarial explanations that use authoritative evidence, neutral tone and expert-style reasoning preserved nearly all trust compared with benign explanations, making users equally likely to accept incorrect outputs.arxiv | Shutong Fan provider id |
2026-02-03 | 3 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 3 cumulative citations. This is a coverage summary, not an author score or h-index.