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:
2146375017
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Z. Liu (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Org Design: 1 paper
Claim outcomes
- Firm Revenue: 1 paper
- Governance And Regulation: 1 paper
- Adoption Rate: 1 paper
- Ai Safety And Ethics: 1 paper
- Organizational Efficiency: 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 decision-making concentrates legal risk in a few complex, drifting models; insurers can price that exposure using confusion-matrix expected-loss models adjusted for drift and tail uncertainty, while stronger governance, documentation and MLOps practices both lower expected losses and become underwriting prerequisites.openalex | Z. Liu provider id |
2026-01-31 | 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.