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
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5061706548
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Anupama Balakrishnan (openalex, provider refresh)
- Anupama Balakrishnan (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
Claim outcomes
- Adoption Rate: 1 paper
- Task Allocation: 1 paper
- Decision Quality: 1 paper
- Governance And Regulation: 1 paper
- Organizational Efficiency: 1 paper
- Other: 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 |
|---|---|---|---|
| Auditors say AI can sharply speed up and improve audits—particularly fraud detection—but uptake is slowed by high costs, a shortage of skilled staff, and data-security worries, making a human-plus-AI hybrid the expected outcome.openalex | Anupama Balakrishnan 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.