Making AI-assisted decisions visible and auditable
Ansh PuniaIndependent AI & systems builder
A simple structure for showing inputs, confidence, human approval and the actions a system took.
01Record the information the decision used
Keep the relevant input, model or workflow version and any retrieved source material required to explain the result. Avoid collecting unrelated sensitive data merely because the technology makes storage easy.
02Separate recommendation from action
The interface should show what the system inferred, its confidence or uncertainty and the action proposed. Consequential actions need explicit approval rules and a clear way to correct the underlying information.
03Make the operating history useful
Logs should help support teams resolve a specific case and help owners identify broader patterns. Retention, access and redaction rules matter so auditability does not become uncontrolled data accumulation.
Before moving forward, make these visible.
- Relevant input and version recorded
- Recommendation separated from action
- Approval and correction path
- Purposeful retention and access rules
AI automation
This thinking is part of how I approach AI automation work—scoped around the business outcome, built in stages you can review, and handed over with the accounts and documentation in your name.
See how AI automation projects run
