Immigration New Zealand profiles overstayers using predictive data model
In April 2018 reporting revealed Immigration New Zealand had been piloting a data‑modelling programme that used historical demographic and outcome data to build risk profiles of overstayers. Officials described it as a pilot to prioritise cases likely to cause 'harm,' while critics alleged it enabled racial profiling and lacked adequate oversight. The disclosure prompted public debate and scrutiny over the fairness of automated profiling in immigration enforcement.
The system modelled past overstayers' demographics and behaviours to predict who was 'likely to commit harm,' effectively profiling groups rather than assessing individuals.
Key facts
- What
- In April 2018 reporting revealed Immigration New Zealand had been piloting a data‑modelling programme that used historical demographic and outcome data to build risk profiles of overstayers.
- Incident date
- Apr 5, 2018
- Who
- Immigration New Zealand
- Failure mode
- Policy Violation
- AI surface
- Algorithmic Decision
- Severity
- High
What happened
Immigration New Zealand ran a pilot data‑modelling programme that used historical records (including age, gender, ethnicity, past convictions and unpaid hospital debts) to build risk profiles of people who had overstayed visas. The modelling was used to identify demographic groups allegedly more likely to cause 'harm' and to prioritise intervention, including faster deportation rather than prosecution. The programme was reported publicly in April 2018 and drew criticism that it amounted to racial profiling. Government officials said the work was a pilot and that a range of data was considered.
What broke inside the model
- 01 · TriggerA prompt pushes against a deployment boundary.
- 02 · Model stepThe model produces the disallowed output.
- 03 · Control gapNo enforcement blocks it at generation time.
- 04 · FailureThe output crosses the policy line.
- 05 · ConsequenceA limit the business set is breached in public.
The output crosses a policy boundary the deployment had defined.
The system relied on historical demographic and outcome data as proxies for individual risk, producing group-level profiles that could be applied to individuals without transparent safeguards. There was limited public oversight or an accessible fairness review process reported, which critics said allowed biased assumptions to influence enforcement decisions.
What it cost
Sources
Cite this entry
https://failureindex.ai/failures/immigration-zealand-profiles-overstayers-using-predictiAI Failure Index. "Immigration New Zealand profiles overstayers using predictive data model" (FI-0472). Realm Labs. https://failureindex.ai/failures/immigration-zealand-profiles-overstayers-using-predicti (indexed Jun 10, 2026).Data fields CC-BY 4.0, prose citation permitted. Incident ID FI-0472. Full dataset at /data.
Note from Realm Labs, the Index steward
How Realm fits
- Prism
- OmniGuard
This entry sits in the index's predictive wing: a system that scores, ranks, perceives, or steers rather than generates. Realm's runtime layer is built for the generative and agentic systems now moving into these same decision seats, where it watches a model's internal state and holds an unsupported claim or an unchecked action before it commits. The control gap on this record, an automated decision that reached people with no runtime check in front of it, is the same gap. The index keeps predictive failures on the record because the pattern carries straight into the systems shipping today.