AI Governance Risks
Cross-source consensus on AI Governance Risks from 1 sources and 4 claims.
1 sources · 4 claims
How it works
Risks & contraindications
Highlighted claims
- Governance of SaMD and AIaMD should cover the full product lifecycle from design through postmarket surveillance and iterative updating. — Capacity building for ethical use of artificial intelligence in health: protocol for a scoping review of training initiatives and gaps in Africa
- AI deployment introduces ethical, regulatory, and governance challenges in health systems. — Capacity building for ethical use of artificial intelligence in health: protocol for a scoping review of training initiatives and gaps in Africa
- AI algorithms can embed or amplify bias. — Capacity building for ethical use of artificial intelligence in health: protocol for a scoping review of training initiatives and gaps in Africa
- AI systems can make decision pathways difficult to understand and shift accountability between clinicians and developers. — Capacity building for ethical use of artificial intelligence in health: protocol for a scoping review of training initiatives and gaps in Africa