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
6Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
1795294
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- M. Dras (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Inequality: 1 paper
- Productivity: 1 paper
Claim outcomes
- Ai Safety And Ethics: 1 paper
- Governance And Regulation: 1 paper
- Adoption Rate: 1 paper
- Other: 1 paper
- Job Displacement: 1 paper
- Market Structure: 1 paper
- Research Productivity: 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 |
|---|---|---|---|
| Most LLM misalignments are choices, not fate: problems often come from data, objectives, and deployment decisions, not just model scale. Adopting a Flourishing–Justice–Autonomy approach—pluralistic evaluation, transparency, and participatory governance—would reduce harms and address economic incentive failures by treating alignment as a public‑good, ongoing process.openalex | M. Dras provider id |
2026-03-13 | 3 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 3 cumulative citations. This is a coverage summary, not an author score or h-index.