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
4Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2292291406
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Maitraye Das (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Inequality: 1 paper
- Labor Markets: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Hiring: 1 paper
- Adoption Rate: 1 paper
- Employment: 1 paper
- Governance And Regulation: 1 paper
- Other: 1 paper
- Worker Satisfaction: 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 |
|---|---|---|---|
| AI resume-screening tools frequently misrepresent blind and low-vision applicants and create dehumanizing interactions; affected job seekers respond by using assistive workarounds, sharing AI literacy in peer networks, and sometimes refusing to engage with automated systems. Design changes that center disability perspectives and acknowledge interdependent job-search practices are needed to avoid creating new systemic barriers.arxiv | Maitraye Das provider id |
2026-01-17 | 2 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 2 cumulative citations. This is a coverage summary, not an author score or h-index.