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
3Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2238589672
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- William Yeoh (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Labor Markets: 1 paper
- Productivity: 1 paper
Claim outcomes
- Ai Safety And Ethics: 1 paper
- Decision Quality: 1 paper
- Adoption Rate: 1 paper
- Governance And Regulation: 1 paper
- Organizational Efficiency: 1 paper
- Output Quality: 1 paper
- Research Productivity: 1 paper
- Consumer Welfare: 1 paper
- Error Rate: 1 paper
- Firm Revenue: 1 paper
- Market Structure: 1 paper
- Other: 1 paper
- Regulatory Compliance: 1 paper
- Skill Acquisition: 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 |
|---|---|---|---|
| Marrying formal argumentation with large language models could make AI decisions inspectable and contestable, creating demand for verifiable AI services in regulated sectors; at the same time it will shift experts toward adjudication and oversight and create new governance needs.arxiv | William Yeoh provider id |
2026-03-16 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.