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:
1388372395
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- F. Doshi-Velez (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Research Productivity: 1 paper
- Team Performance: 1 paper
- Adoption Rate: 1 paper
- Task Allocation: 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 |
|---|---|---|---|
| A new collaboration benchmark shows humans and AI replicate some human–human coordination patterns but diverge sharply on establishing common ground and repairing misunderstandings. The lab study validates the task’s theoretical grounding but flags limited generality: human-AI teams break down in predictable ways that could constrain real-world productivity gains.arxiv | F. Doshi-Velez provider id |
2026-02-24 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.