Algorithmic governance / public serviceschild welfare and public servicesParticipation documented
Toward Algorithmic Accountability in Public Services
Wellington, New Zealand
The project ran workshops with affected families, service providers, and public-service workers to surface accountability demands around automated decision systems.
algorithmic accountabilitypublic-interest designparticipatory governanceproblem formulationauditinggovernanceevaluation
- Region
- Multi Region
- Lead organization
- Toi Āria; Massey University; Carnegie Mellon University; USC; AUT
- Organization type
- university + research institute collaboration
- Technology group
- algorithmic decision systems in public services
- Activity status
- published_case
- Start year
- 2018
- Last updated
- Mar 25, 2026
- Participation mode
- affected-community workshops + cross-sector dialogue
- Participants
- Affected families; agency workers; service providers; researchers
- Methods
- workshops; scenario discussion; collaborative critique; cross-sector dialogue
- AI lifecycle stages
- problem formulation; auditing; governance; evaluation
- Evidence summary
- The project ran workshops with affected families, service providers, and public-service workers to surface accountability demands around automated decision systems.
- Atlas assessment
- Core · High confidence · evidence grade A
- Uncertainty
- Public evidence indicates that stakeholders directly shaped AI design, data, deployment, evaluation, or oversight; residual uncertainty concerns the exact degree of shared decision power rather than project existence.
- Participation group
- Participatory Research
- Technology description
- algorithmic decision systems in public services
- Funding
- Not documented
- Region of activity
- international
- Verification status
- Live Verified
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