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:
2247831049
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Wael Elmedany (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Inequality: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
- Other: 1 paper
- Adoption Rate: 1 paper
- Decision 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 |
|---|---|---|---|
| Academic research on AI credit scoring delivers strong predictive results but treats fairness and explainability as separate issues, leaving scarce guidance for regulated, real‑world use; the literature rarely evaluates integrated approaches that meet oversight and accountability requirements.openalex | Wael Elmedany provider id |
2026-02-03 | 8 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 8 cumulative citations. This is a coverage summary, not an author score or h-index.