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
0Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2455758939
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Geoffrey Odoch (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
- Organizational Efficiency: 1 paper
- Adoption Rate: 1 paper
- Decision Quality: 1 paper
- Error Rate: 1 paper
- Governance And Regulation: 1 paper
- Market Structure: 1 paper
- Task Allocation: 1 paper
- Training Effectiveness: 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 |
|---|---|---|---|
| AI supercharges audit analytics and fraud detection but risks hollowing out professional judgment; without clearer standards, validation protocols and training, efficiency gains could come at the expense of audit quality and accountability.openalex | Geoffrey Odoch provider id |
2026-07-29 | 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.