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 dates unavailable. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5127748696
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Lalithareddy Badam (openalex, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Org Design: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Ai Safety And Ethics: 1 paper
- Market Structure: 1 paper
- Research Productivity: 1 paper
- Adoption Rate: 1 paper
- Decision Quality: 1 paper
- Firm Productivity: 1 paper
- Firm Revenue: 1 paper
- Governance And Regulation: 1 paper
- Hiring: 1 paper
- Organizational Efficiency: 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 |
|---|---|---|---|
| Explainability helps, but only with rules and design: readable, actionable explanations increase trust and accountability in high‑risk AI applications only when paired with human‑centered design, clear governance and auditability; without institutional safeguards, explanation efforts can fail or backfire.semantic_scholar | Lalithareddy Badam provider id |
Fetched 2026-03-12 | 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.