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
0Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2333635071
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Jay Barach (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Labor Markets: 1 paper
Claim outcomes
- Inequality: 1 paper
- Decision Quality: 1 paper
- Ai Safety And Ethics: 1 paper
- Governance And Regulation: 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 |
|---|---|---|---|
| A per-decision counterfactual audit identifies the exact applicants an automated screener harmed and explains why: neutralizing protected-proxy features and re-scoring pinpoints flips with high accuracy on standard benchmarks, massively improving targeted review efficiency — though fixing flagged cases does not fully erase group disparities because legitimate features can still proxy for protected attributes.arxiv | Jay Barach provider id |
2026-08-21 | 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.