Structured intake that resists volume-padding
An intake form built from constrained fields (drop-downs, date pickers, document-upload requirements) with conditional branching, ending in an explicit escape pathway to human review.
Free-text forms are trivially padded by AI agents. An agent can generate lengthy, plausible-sounding narratives that meet surface-level quality thresholds without reflecting genuine human circumstances. Systems that assess applications partly on volume or detail of supporting narrative become vulnerable.
Government needs to assess an application on verifiable data tied to external sources, not on the volume or fluency of supporting narrative, so that an agent's ability to generate plausible prose does not raise an applicant's apparent merit.
Every structured intake must include a 'my situation doesn't fit these options' free-text escape hatch, preferably routed to a human rather than rejected, so that over-structuring does not itself become sludge for people whose circumstances do not fit predefined categories.
Constrained field types and external-record links (tax file number, Medicare) carry the assessment instead of free-text narrative, with a mandatory 'this doesn't capture my situation' field that triggers human review.
No surface has been built yet; the approach above is the brief for one.
- Established Headline
As a form design principle.
- Emerging
As a deliberate anti-AI-padding strategy.
Structured intake is highly transferable but requires careful design:
- Field constraints (drop-downs, date pickers, document-upload requirements) create verifiable data points that are harder to fabricate than free text.
- Conditional logic (showing questions based on prior answers) makes bulk template generation harder because the path through the form varies.
- Evidence requirements tied to external data sources (tax file number, Medicare record, employer) shift verification from narrative to data.
- Risk: Over-structuring intake can itself become sludge for people whose circumstances do not fit neatly into pre-defined categories. Good design requires an escape hatch, a way to flag that structured fields do not capture a person's situation, triggering human review.
The failure mode is an automated process drawing adverse inferences from data the person could not contest. Anchoring assessment to verified external data and providing a human-review escape hatch prevents that.