Automation level communication
A consent-style screen that labels the interaction with one of three plain automation levels and, before any consequential action, shows what the agent will do and what data it will access.
As agents capable of varying degrees of automation are deployed across government services, the degree will differ from one service and action to the next, and the tools to build them are now widely available. Without a shared frame for naming that degree, the same agent behavior reads as helpful efficiency to one citizen and as threatening opacity to another, and neither can tell how much a human actually decides.
Government needs a citizen to be able to tell how much of a given action a human decides versus a machine, in terms they can grasp without technical literacy, so the same behavior is not read as helpful by one person and as opacity by another, and so it addresses both misuse (over-reliance) and disuse (under-use).
Use concrete, action-oriented language rather than abstract levels ("A person will check this before it's final" rather than "Level 2 supervised automation"). Test comprehension with diverse groups including low digital literacy, cognitive disability, and limited English. Provide worked examples of what each level means for the specific service.
The automation degree of this action is shown as one of three concrete labels (AI-assisted, AI-recommended, AI-decided) alongside an open-banking-style pre-action consent panel.
No surface has been built yet; the approach above is the brief for one.
- Established Headline
The labeling response is established in theory and proven in adjacent domains (automotive autonomy levels, open-banking consent).
- Emerging
As a public-facing scheme for government AI services.
Parasuraman & Riley (1997) — Use, Misuse, Disuse, Abuse. Identified four failure modes of human-automation interaction. Misuse (over-reliance) follows from insufficient transparency about limitations; disuse (under-utilization) follows from excessive false alarms or unclear capability communication. Both are addressable through clear automation-level signaling.
SAE J3016 as a communication device. Beyond its technical function, the SAE levels framework succeeded as a communication tool: "Level 2" or "Level 4" conveys meaningful information to non-experts. "Level 1: AI assists, you decide" is more comprehensible than "supervised machine-learning-augmented decision support."
Open banking consent screens (PSD2/SCA). PSD2 established a pattern for communicating automated actions in financial services: explicit consent screens stating what data will be accessed, by whom, and for what purpose, with the right to revoke at any time. The three-factor model (knowledge, possession, inherence) gives graduated assurance proportional to transaction risk.
Graduated scope reduction with temporal decay. In delegated-authority systems, authorization scope can be tied to a risk-tiered expiry — a short window for high-risk actions, a longer one for low-risk — so delegated authority does not persist beyond its justified window.
High transferability. Government services suit a simple, public-facing automation-level vocabulary. A three-level scheme is likely sufficient:
- "AI-assisted": a human makes the decision; the AI helps gather and organize information.
- "AI-recommended": the AI proposes a decision; a human reviews and approves it.
- "AI-decided": the AI decides within defined rules; a human is available for review on request.
The open-banking consent pattern transfers well: before any agent action with consequences, show the citizen what the agent will do, what data it will access, and offer explicit consent or decline.
The failure mode is an automated decision presented as if a human exercised judgment, hiding the absence of meaningful review. Labeling determinations honestly as 'AI-decided' makes that absence legible to the people affected.