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
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2500056065
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Kosuke Kitahara (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Labor Markets: 1 paper
Claim outcomes
- Hiring: 1 paper
- Ai Safety And Ethics: 1 paper
- Governance And Regulation: 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 |
|---|---|---|---|
| Open-weight LLMs used for recruitment respond to job-ad language in ways that disadvantage protected groups: agentic wording substantially lowers recommendations for female personas (rrb ≈ 0.31), while coded-exclusion language markedly reduces both recruiter scores and expressed interest for non-White personas (rrb ≈ 0.65–0.76). Label-ablation and embedding tests implicate explicit demographic labels and encoded representations, suggesting practical pre-deployment posting-language audits can flag adverse impact under regulatory thresholds.openalex | Kosuke Kitahara provider id |
2026-09-16 | 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.