Human accountability
Identify who owns consequential decisions and which actions need human review before deployment.
RESPONSIBLE AI
AI should be useful, bounded and accountable.
Responsible-AI controls depend on the use case, data, model, tools and deployment environment. This page describes an approach, not a claim that every control is independently audited or implemented in all systems.
Data protection
Confirm data sources, permissions, sensitivity, retention and model-provider boundaries for each use case.
Evaluation
Test representative, ambiguous and failure cases against agreed criteria; record environment, model and configuration.
Transparency and limitations
Document intended use, known limits, escalation paths and where the system should not be relied on.
Access and security
Restrict system and tool permissions to the task and review them in the actual deployment context.
Monitoring and change
Agree operational ownership, monitoring, incident handling and re-evaluation when models or workflows change.
Project-specific review
Before work proceeds, agree relevant data handling, access boundaries, evaluation scope, human responsibilities, monitoring and incident arrangements. No certification, universal accuracy level or risk-free operation is claimed.
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