Public services cannot be accurate only for citizens who operate in English.
When AI mediates access to a public service, uneven performance across languages becomes an equity problem rather than a quality metric. Systems need to be evaluated across the full population they serve, and the evidence needs to be defensible.
Common failure modes.
- Service quality that varies sharply by language group
- Local documents and records the system cannot read
- Assumptions about connectivity, devices and literacy
- No defensible evidence of performance across communities
How we work in government & public services.
Citizen-facing evaluation
Performance measured across every language group served.
Records and documents
Datasets built from the document formats actually in use.
Equity testing
Whether outcomes differ systematically between groups.
Clear reporting
Findings written to withstand scrutiny, with evidence attached.
AI programs built around public-sector obligations.
Public bodies answer to regulators, auditors and the citizens they serve. Where data originates, who may handle it and how long it is kept are rarely negotiable, so we design the program around those constraints from the outset.
Data Residency
Design collection, processing and storage around in-country or approved regional environments.
Controlled Access
Limit handling to approved personnel and environments, with activity recorded for audit.
Equity Evaluation
Measure whether service quality differs systematically between language groups.
Provenance
Record where data came from, under what consent, and what may be done with it.
The applicable model depends on your regulatory, contractual and operational requirements. See Sovereign AI for how we design against them.
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