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:
2400811209
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Subur Harahap (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Labor Markets: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Output Quality: 1 paper
- Ai Safety And Ethics: 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 |
|---|---|---|---|
| Algorithmic evaluation appears to improve perceived audit accuracy, efficiency and transparency by boosting system reliability; a 120-respondent survey of auditors and IT staff suggests audit firms that embed algorithmic checks could increase trust—though evidence rests on self-reports and cross-sectional data.openalex | Subur Harahap provider id |
2026-07-28 | 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.