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:
A5107534548
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Shay Tsaban (openalex, provider refresh)
- Shay Tsaban (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Inequality: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
- Ai Safety And Ethics: 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 |
|---|---|---|---|
| Which fairness metric an auditor picks can reverse an AI system’s bias verdict: across four systems and 91,572 defensible computations the regulatory pass/fail flips in every case, and the reported figure moves far more than sampling error. Published disclosures almost never document the choices, but requiring a stated reference computation and consistency disclosure would eliminate most of the arbitrariness.openalex | Shay Tsaban provider id |
2026-08-12 | 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.