Andrew Sporle

Indigenous peoples experience a paradox in official statistics: deficit framing and surveillance through state data systems, alongside an urgent need for evidence to monitor inequities and advocate for policy change. In Aotearoa New Zealand, existing monitoring systems often fail to capture the diversity and intersectional realities of Māori communities. This article outlines the rationale and conceptual foundations for the Te Ao Mārama Māori Health, Wellbeing, and Social Survey Panel, a Māori‐governed probabilistic longitudinal survey infrastructure. Drawing on Indigenous statistics, Māori data sovereignty scholarship and the whakapapa of Māori‐led survey research, we review limitations in national monitoring systems, including the New Zealand Health Survey, Te Kupenga, General Social Survey, the Household Labour Force Survey and Integrated Data Infrastructure. We argue that these systems lack sufficient scale, frequency, cultural relevance and Māori governance to support robust intersectional analyses, contributing to the statistical invisibilisation of tāngata whaikaha Māori, rural communities, kaumātua and other priority groups. The Te Ao Mārama Panel responds by combining probabilistic sampling with kaupapa Māori methodologies and tikanga‐based governance, with Te Ao Mārama Aotearoa Trust acting as data kaitiaki. This infrastructure enables Māori‐defined monitoring of health, wellbeing and social attitudes over time, supports disaggregated analyses and provides capacity for rapid ‘pulse’ surveys during crises. By embedding Indigenous data sovereignty principles within survey design and governance, Te Ao Mārama strengthens analytic visibility of Māori realities and provides a sustainable platform to inform equitable policy, accountability and collective flourishing.

Seasonal influenza virus circulation was eliminated in Aotearoa (New Zealand) from 2020 to 2022, following the nation's stringent public health response to the COVID-19 pandemic. Here, we generate nearly 4,000 influenza virus genomes captured over a decade of national surveillance (2013–2022), encompassing influenza A(H3N2) and A(H1N1)pdm09 subtypes and B/Victoria and B/Yamagata lineages. We show that the reintroduction of influenza virus in 2022, was marked by an early and sharp epidemic dominated by the influenza A/H3N2 clade 3C.2a1b.2a.2a.1. Bayesian phylodynamic inference revealed a collapse in influenza virus diversity during border closures, with the diversity of influenza A(H3N2) partially recovering in 2022. Spatial diffusion modelling of the monophyletic A(H3N2) subclade in 2022 revealed strong source-sink dynamics, with the three healthcare regions in the North Island seeding transmission into the South Island. B/Yamagata lineages remained absent from our surveillance post-pandemic, supporting global evidence of its extinction. These findings highlight how prolonged interruption of viral transmission can reshape epidemic dynamics and reduce viral diversity, underscoring the importance of genomic surveillance in understanding and anticipating the evolution and re-emergence of seasonal pathogens.

Māori, the Indigenous people of Aotearoa New Zealand, experience wide-ranging inequities compared with non-Māori. This survey aimed to explore the holistic health, wellbeing, and disability experiences of New Zealand’s Indigenous Māori population from a Māori worldview, addressing gaps in culturally relevant data often overlooked by standard health surveys. A robust cross-sectional survey was conducted with 7359 participants of Māori descent using Kaupapa Māori Research principles. Data were analysed using the Te Pae Māhutonga framework, a Māori health promotion model. Participants demonstrated strong cultural identity, with 32.3% understanding spoken Māori fairly well and 97.3% defining a broad non-nuclear concept of whānau (family). While over half reported high life satisfaction, 58.4% experienced discrimination, mainly based on ethnicity and appearance. Access to healthcare revealed that 32.6% were unable to contact a general practitioner due to cost. Socioeconomic challenges were prevalent; nearly a quarter borrowed from family or friends to meet daily living costs, and over a third economized on fresh produce to save money. This study reveals significant gaps in mainstream health data and demonstrates that a culturally aligned, methodological approach is feasible and crucial for informing policies that address the needs and rights of Māori, as guaranteed under Te Tiriti o Waitangi. These results could inform global, indigenous research addressing culturally relevant health, wellbeing and disability inequities.

A high-level précis of the Synthesis Report can be found in the Summary for Policymakers Recommendations for a Global AI Auditing Framework: Summary of Standards and Features. The growing integration of artificial intelligence (AI) into critical sectors of society, from healthcare to education, has the potential to support widespread social transformation and progress. However, AI systems also have the power to perpetuate biases, deepen inequalities, and cause environmental harm. Accurately evaluating the risks and benefits of an AI system requires a careful audit. Current approaches to auditing, however, rarely involve independent auditors, provide sufficient evidence, or account for global impacts. Policymakers urgently need a comprehensive global framework for AI audits that validates genuine benefits and risks. The IPIE’s Scientific Panel on Global Standards for AI Audits set out to independently establish global, cross-disciplinary scientific consensus on what makes an audit effective and trustworthy. The Panel consisted of 16 experts from computer science, the social sciences, and the humanities, with expertise spanning generative AI systems, algorithmic auditing, indigenous data sovereignty, data journalism, and the relationship between civil society, human rights, and AI.

This Summary for Policymakers provides a high-level précis of the Synthesis Report Towards A Global AI Auditing Framework: Assessment and Recommendations. The growing integration of artificial intelligence (AI) into critical sectors of society, from healthcare to education, has the potential to support widespread social transformation and progress. However, AI systems also have the power to perpetuate biases, deepen inequalities, and cause environmental harm. Accurately evaluating the risks and benefits of an AI system requires a careful audit. Current approaches to auditing, however, rarely involve independent auditors, provide sufficient evidence, or account for global impacts. Policymakers urgently need a comprehensive global framework for AI audits that validates genuine benefits and risks. The IPIE’s Scientific Panel on Global Standards for AI Audits set out to independently establish global, cross-disciplinary scientific consensus on what makes an audit effective and trustworthy. The Panel consisted of 16 experts from computer science, the social sciences, and the humanities, with expertise spanning generative AI systems, algorithmic auditing, indigenous data sovereignty, data journalism, and the relationship between civil society, human rights, and AI.

This Synthesis Report is a literature review outlining the regulatory, industry, and academic approaches to AI audits. We review 78 articles published in peer-reviewed journals and as preprints, 21 documents from industry associations and standard-setting organizations, and national policy documents and regulations from 20 countries. Based on this review, we identify three key takeaways about the landscape of AI auditing: 1. To accurately assess the potential risks, and impacts of AI systems, we need a trustworthy audit ecosystem with complementary approaches from internal, external, and community auditors. 2. Auditors need better access to data and audit artifacts from the developers and deployers of AI systems. Comprehensive auditability requires documentation and disclosure of an AI system’s model and data components, associated risks and impacts, and easily understandable explanations of its outcomes. 3. Given that the development and use of AI systems impacts communities across the world, audit regimes must account for their global effects. Most existing audits have been conducted in North America, Europe, and other regions of the ‘global north’, with their results typically published in English and focused on effects within these regions. The impacts of AI systems, however, include shifts in social and environmental conditions beyond the immediate development or application contexts of a system.

High-quality evidence on the prevalence and impact of health, wellbeing, and disability among Māori, and other Indigenous peoples, is crucial for mitigating health inequities. Current surveys are predominantly centred within a biomedical paradigm, with the constructs mismatched with Indigenous worldviews. We aimed to develop and deploy an accessible and culturally grounded survey exploring Māori health, wellbeing, and disability using a Kaupapa Māori Research methodology. An extensive codesign process with Māori community partners interrogated all aspects of the design to ensure the process and outcomes met the needs of Māori. A large-scale, nationally representative survey of people of Māori descent was conducted. We used a multi-modal deployment approach that included online and alternate methods of completion. Our analysis included a novel dual-weighting system to ensure generalisability of results to the national Māori population. This achieved a survey of 7230 participants, a sample size comparable with government-administered surveys. The response rate was 11.1%, with 7.3% opting for alternate methods. A high completion rate of 93.4% was observed. This approach demonstrated a high level of engagement, resulting in an unprecedented collection of Māori health, wellbeing, and disability data. This highlights the importance of Indigenous codesign for ensuring accessible and culturally appropriate survey methods.