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:
A5010781163
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Dinesh Bidari (openalex, provider refresh)
- Dinesh Bidari (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Productivity: 1 paper
Claim outcomes
- Research Productivity: 1 paper
- Governance And Regulation: 1 paper
- Employment: 1 paper
- Fiscal And Macroeconomic: 1 paper
- Inequality: 1 paper
- Regulatory Compliance: 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 |
|---|---|---|---|
| Academic research on AI for tax compliance has surged since 2020, dominated by machine-learning fraud detection but rapidly pivoting toward explainability and governance; the authors synthesize 527 Scopus records into a practical five-step implementation framework spanning risk design, data integration, model choice, governance, and continuous monitoring.openalex | Dinesh Bidari provider id |
2026-08-25 | 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.