AI Development for Government
AI development for government is the practice of building public-sector AI that can be explained, appealed and audited. It covers decision transparency and reason-giving, bias testing across the whole population served, records retention that satisfies disclosure law, human review where entitlements are affected, and clear boundaries on where AI should not be used at all.
Why does explainability matter more in public-sector AI?
A person refused a benefit, flagged for review or placed in a queue by an automated system generally has a right to know why and to challenge the outcome. That right is meaningless if the reason is that a model produced a score, so the system has to attribute an outcome to identifiable factors at the level of the individual.
This constrains architecture rather than merely adding documentation. Where a determination affects entitlement, interpretable models with traceable reasoning are usually the right choice, and complex models are better placed in advisory or triage roles where a human makes the determination and can articulate it.
How do you test fairness when you serve everyone?
A commercial system can select its market. A public body cannot, so evaluation has to cover the full population served — including groups too small to matter statistically to a commercial product but who are entitled to equal treatment regardless.
That means deliberately sampling for under-represented groups rather than relying on a random split, measuring outcome disparities and not just aggregate accuracy, and treating a performance gap for a small group as a defect rather than an acceptable trade-off. It also means being willing to conclude that a system is not fit to deploy.
Where should government AI not be used, and what is our scope?
Some determinations should not be automated even when they could be — because the consequence is severe, because the judgement is genuinely contextual, or because public trust in the process matters more than the efficiency gained. Deciding this belongs at the start of a programme, not after a pilot has created momentum, and we will say so when we think a proposed use falls into that category.
On scope, plainly: **Appsierra is not a FedRAMP-authorised cloud service provider, is not a Third Party Assessment Organization, and does not issue or sponsor authorisations.** We build and evaluate systems aligned to NIST AI RMF and NIST SP 800-53 and produce the technical evidence an assessment consumes, working alongside whoever holds the authorisation boundary.
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