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
1Unique collaborators
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5133590889
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Lu Chao (openalex, provider refresh)
- Lu Chao (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Org Design: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Automation Exposure: 1 paper
- Error Rate: 1 paper
- Research Productivity: 1 paper
- Task Allocation: 1 paper
Papers in the OpenAlex view
Latest stored OpenAlex 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 |
|---|---|---|---|
| A new value-theoretic framework argues that delegating decisions to agentic AI is safest at intermediate autonomy: efficiency gains rise with discretion but errors and oversight costs produce diminishing and eventually negative returns, while better accountability can raise the level of safe delegation; incident-data analysis and a small synthesis of studies provide suggestive empirical support for this governance frontier.openalex | Lu Chao provider id |
2026-09-02 | 0 |
Citation observation summary
OpenAlex 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.