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:
2336890400
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Izlawanie Muhammad (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Org Design: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
- Regulatory Compliance: 1 paper
- Other: 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 |
|---|---|---|---|
| Tax officials say AI tools improve detection and compliance but practical barriers limit impact: a Palestinian revenue department survey finds predictive analytics and automated auditing are seen to boost accuracy, yet concerns over data privacy, high costs and staff unfamiliarity threaten widescale adoption.openalex | Izlawanie Muhammad provider id |
2026-01-14 | 16 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 16 cumulative citations. This is a coverage summary, not an author score or h-index.