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  • Terms of reference

    The terms of reference for the Data Ethics Advisory Group outline the purpose, role, and membership of the Group.

  • Nest Finder - Accessing essential travel information while on the road

    When tourists are travelling around New Zealand, there is a vast amount of information they need to access including where to stay and what to see. This is where the handy Nest Finder app comes in.

  • Algorithm Assessment Report

    This report summarises the self assessments of fourteen government agencies and their use of algorithms, focusing on areas that most directly impact decisions related to people.

    Algorithm…

  • NZGOAL-SE guidance note 2

    Getting started

    Don’t repeat yourself

    Where and how to publicly release your code?

    What to include in your first code release

    Working in the open

    Safe configuration practices

    Release early and often

    Version…

  • Guidance note 4

    ContentsIntroduction and context

    Copyright, databases and datasets

    The law as stated in NZGOAL

    Developments since NZGOAL was approved in 2010

    Suitability of Creative Commons licences for copyright databases and datasets

    Consideration…

  • New videos explore 'Are data taonga?'

    New videos by Stats NZ brings panellists together to examine and debate Māori perspectives about the way we collect, share, and use data. 

     

  • Principle 1: Pūkenga & Whakapapa

    Principle 1: Have appropriate expertise, skills, and relationships with communities. This principle includes ngā tikanga Pūkenga (skills and expertise) and Whakapapa (genealogy).

  • Ngā Tikanga Paihere

    This guidance is a good option if you want Te Ao Māori principles to inform your data practice. It is also a good framework for thinking about working with communities, and ensuring your data practices occur in good faith.

  • Ngā Tikanga Paihere

    Ngā Tikanga Paihere draws on 10 tikanga (Te Ao Māori/Māori world concepts) to help you establish goals, boundaries, and principles that guide and inform your data practice.

  • Challenge accepted: how transparent can we be with our algorithms?

    At the first community of practice hui, interested agencies faced the task of figuring out what good transparency could, and should, look like.

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