Make citizen-facing AI reviewable by design
Public-sector AI fails commercially and operationally when it cannot be explained, challenged, or halted before it affects a citizen outcome. KLA creates the runtime control path needed for oversight without forcing agencies into a full technology reset.
The workflow pain
Oversight teams do not need another slide about responsible AI. They need a concrete path for blocking, reviewing, and replaying the decisions that matter.
Casework and eligibility
Require human review before a recommendation changes a citizen outcome, notice, or prioritisation decision.
Transparency and oversight
Retain the exact decision lineage needed for appeals, inspector reviews, and internal accountability.
Govern in place
Add controls to existing public-sector systems without forcing a full rip-and-replace of operational infrastructure.
Governed examples
- Eligibility assistants that recommend but cannot finalise without review
- Casework copilots that draft but cannot send notices without oversight
- Internal knowledge assistants that are governed before they influence public outcomes
What oversight asks for
- Reviewer and agency workflow path for every escalated decision
- Source context, tool calls, and output lineage for the final action
- Retention and replayability for oversight, appeals, and internal investigation
When AI helps decide who gets a public service, it is high-risk by classification
The EU AI Act lists AI used to determine access to essential public services and benefits among its high-risk categories (Annex III). That triggers concrete obligations — meaningful human oversight, record-keeping, and transparency — that a generic responsible-AI statement does not satisfy. KLA turns those obligations into runtime controls on the decisions that actually affect citizens.
The failure mode that draws scrutiny
An eligibility or casework assistant moves from drafting a recommendation to effectively setting a citizen outcome, and the agency cannot reconstruct what evidence produced it, who reviewed it, or how a person would appeal. In the public sector that is not a bug report — it is an accountability and legitimacy problem. KLA closes the gap at the point of decision rather than after the complaint.
- 01
Classify the decision
The EU AI Act treats AI used to decide access to essential public services and benefits as high-risk (Annex III). KLA marks those steps so they cannot execute through the same path as low-stakes drafting.
- 02
Hold the consequential action
Before an eligibility recommendation, a citizen notice, or a prioritisation decision commits, the runtime gate returns allow, warn, require approval, or block — keeping a human in control of the outcome.
- 03
Seal reviewable lineage
Each governed decision is recorded with its source context, reviewer, and final action, producing the record-keeping and appeal trail oversight bodies and inspectors expect.
Related workflow blueprints
The same govern-in-place pattern — intercept the consequential action, route it to a named human, seal the lineage — is documented in detail for other regulated workflows. Use them as a reference for how KLA maps a binding regime to runtime checkpoints on an agent you already built.