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
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5138436739
ORCID evidence
No valid ORCID is stored.
Observed aliases (4)
- Alcorn, Daniel S. (openalex, provider refresh)
- Alcorn, Daniel S. (openalex, source metadata)
- Daniel S. Alcorn (openalex, provider refresh)
- Daniel S. Alcorn (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Inequality: 1 paper
- Labor Markets: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
- Adoption Rate: 1 paper
- Inequality: 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 |
|---|---|---|---|
| Opaque hiring and firing algorithms risk hiding systematic discrimination behind a veneer of objectivity; the article recommends treating algorithmic systems as the relevant employment practice, shifting disclosure burdens after disparities are shown, requiring human-in-the-loop review and auditability, and creating regulatory safe harbors to preserve enforcement without unduly hampering efficiency.openalex | Alcorn, Daniel S. provider id |
2026-01-01 | 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.