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:
2353458869
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Tinglong Dai (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Inequality: 1 paper
- Innovation: 1 paper
Claim outcomes
- Adoption Rate: 1 paper
- Consumer Welfare: 1 paper
- Task Allocation: 1 paper
- Decision Quality: 1 paper
- Output Quality: 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 |
|---|---|---|---|
| Liability aimed at curbing algorithmic disparity can paradoxically limit disadvantaged patients’ access to AI and distort firm investment; higher legal exposure may later restore access by forcing firms to improve accuracy, and blanket equal-accuracy mandates can worsen outcomes by skewing incentives.arxiv | Tinglong Dai provider id |
2026-08-12 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.